2024-03-29T00:47:37Zhttp://oai-repositori.upf.edu/oai/requestoai:repositori.upf.edu:10230/246472018-02-19T10:29:52Zcom_10230_5963col_10230_24646
Zapata González, José Ricardo
2015-07-24T12:48:12Z
2015-07-24T12:48:12Z
2015
http://hdl.handle.net/10230/24647
The dataset DatasetVocal contains 75 excerpts with highly predominant vocals in WAV format and has been used for beat tracking evaluation.
Submitted by Marina LOSADA YÁÑEZ (marina.losada@upf.edu) on 2015-07-24T12:48:12Z/nNo. of bitstreams: 2/nDatasetVocal.zip: 230994667 bytes, checksum: 0860dc6f396ecd8f02ff6b796443ba40 (MD5)/nlicense_rdf: 1089 bytes, checksum: 0a703d871bf062c5fdc7850b1496693b (MD5)
Made available in DSpace on 2015-07-24T12:48:12Z (GMT). No. of bitstreams: 2/nDatasetVocal.zip: 230994667 bytes, checksum: 0860dc6f396ecd8f02ff6b796443ba40 (MD5)/nlicense_rdf: 1089 bytes, checksum: 0a703d871bf062c5fdc7850b1496693b (MD5)
cat
http://hdl.handle.net/10803/123822
CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
info:eu-repo/semantics/openAccess
Comparative evaluation and combination of automatic rhythm description systems [research data]
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/257202018-02-19T10:30:58Zcom_10230_5963col_10230_24646
CompMusic
2016-02-02T14:31:54Z
2016-02-02T14:31:54Z
2016-02-02
CompMusic. Beijing opera percussion pattern dataset [dataset]. Repositori Digital de la UPF: Barcelona, 2016 [citat 17 novembre 2016]. Disponible a: http://hdl.handle.net/10230/25720
http://hdl.handle.net/10230/25720
-Dataset-/n/nThe dataset is a collection of 133 audio percussion patterns spanning five different pattern classes as described below. The scores for the patterns and additional details about the patterns are at: http://compmusic.upf.edu/bo-perc-patterns/n/n-Audio Content-/n/nThe audio files are short segments containing one of the above mentioned patterns. The audio is stereo, sampled at 44.1 kHz, and stored as wav files. The segments were chosen from the introductory parts of arias. The recordings of arias are from commercially available releases spanning various artists. The audio and segments were chosen carefully by a musicologist to be representative of the percussion patterns that occur in Jingju. The audio segments contain diverse instrument timbres of percussion instruments (though the same set of instruments are played, there can be slight variations in the individual instruments across different ensembles), recording quality and period of the recording. Though these recordings were chosen from introductions of arias where only percussion ensemble is playing, there are some examples in the dataset where the melodic accompaniment starts before the percussion pattern ends. /n/n-Annotations-/n/nEach of the audio patterns has an associated syllable level transcription of the audio pattern. The transcription is obtained from the score for the pattern and is not time aligned to the audio. The transcription is done using the reduced set of five syllables described in Table 1 of [1] and is sufficient to computationally model the timbres of all the syllables. The annotations are stored as Hidden Markov Model Toolkit (HTK) label files. There is also a single master label file provided for batch processing using HTK (http://htk.eng.cam.ac.uk/). /n/n-Dataset organization-/n/nThe dataset has wav files and label files. The files are named as /n<pID><InstID>.<extension>/nThe pID is as in Table 1, instID is a three digit identifier for the specific instance of the pattern, and extension can be .wav for the audio file or .lab for the label file. pID ϵ {10, 11, 12, 13, 14}, InstID ϵ {1, 2, ..., NpID}. e.g. The audio file and the label file for the fifth instance of the pattern duotuo is named 12005.wav and 12005.lab, respectively. The master label file is called masterLabels.lab/n/n-Availability of the Dataset-/n/nThe annotations are publicly shared and available to all. The audio is from commercially available releases. It cannot be publicly shared but can be made available on request for non-commercial research purposes. In the future, the dataset would be available for viewing and download through an interface in Dunya (http://dunya.compmusic.upf.edu).
Beijing Opera Percussion Pattern (BOPP) dataset is a collection of 133 audio percussion patterns covering five pattern classes. The dataset includes the audio and syllable level transcriptions for the patterns (non-time aligned). It is useful for percussion transcription and classification tasks. The patterns have been extracted from audio recordings of arias and labeled by a musicologist.
Submitted by Marina LOSADA YÁÑEZ (marina.losada@upf.edu) on 2016-02-02T14:31:54Z/nNo. of bitstreams: 3/nBOPP_Annotations.zip: 28442 bytes, checksum: 17b12c4fb9fef42f25f8a3160f37fefa (MD5)/nBeijing Opera Percussion Pattern Dataset.docx: 19444 bytes, checksum: dbdc9f178ff9432d2707a7f5e16391a1 (MD5)/nlicense_rdf: 1089 bytes, checksum: 0a703d871bf062c5fdc7850b1496693b (MD5)
Made available in DSpace on 2016-02-02T14:31:54Z (GMT). No. of bitstreams: 3/nBOPP_Annotations.zip: 28442 bytes, checksum: 17b12c4fb9fef42f25f8a3160f37fefa (MD5)/nBeijing Opera Percussion Pattern Dataset.docx: 19444 bytes, checksum: dbdc9f178ff9432d2707a7f5e16391a1 (MD5)/nlicense_rdf: 1089 bytes, checksum: 0a703d871bf062c5fdc7850b1496693b (MD5)
eng
http://compmusic.upf.edu/bopp-dataset
Beijing Opera (京剧)
http://hdl.handle.net/10230/25677
info:eu-repo/grantAgreement/EC/FP7/267583
CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
info:eu-repo/semantics/openAccess
Beijing opera percussion pattern dataset
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/262702019-10-22T12:38:18Zcom_10230_5963col_10230_24646
Gómez, Vicenç
Kaltenbrunner, Andreas
Laniado, David
2016-05-11T17:43:34Z
2016-05-11T17:43:34Z
2016-05-10
http://hdl.handle.net/10230/26270
Contain:/n/slashdot/nslashdot_with_comments.tgz (518M): compressed file with raw xml/ntree-YY-MM.mat (128M): conversation threads in Matlab structures format/nFields/n data : struct with fields ; post data/n id : string ; identifier of the post/n user : string ; writer of the news post/n date : double ; seconds/n topics : string ; main topics/n tree : struct with fields ; hierarchical structure of the thread/n data : comment data: id, parentid, score, user and date/n parent : index in tree(index) of the parent (-42 for the root node)/n depth : depth in the thread/n child : vector of children/n/n/barrapunto/nraw_xml.tgz (85M): compressed file with raw xml/ntree-YY.mat (22M): conversation threads in Matlab structures/n/n/wikipedia/nAllArticleTitles.csv.tar.gz (85M): compressed file with article titles/nall_comments.csv.tar.gz (1.6G): compressed file with comments/nWP_tree_raw_data_X.mat (87M): conversation threads in Matlab structures/n/nSoftware: Matlab
This repository contains datasets with online conversation threads collected and analyzed by different researchers. Currently, you can find datsets from different news aggregators (Slashdot, Barrapunto) and the English Wikipedia talk pages. Slashdot conversations (Aug 2005 - Aug 2006) Online conversations generated at Slashdot during a year. Posts and comments published between August 26th, 2005 and August 31th, 2006. For each discussion thread: sub-domains, title, topics and hierarchical relations between comments. For each comment: user, date, score and textual content. This dataset is different from the Slashdot Zoo social network (it is not a signed network of users) contained in the SNAP repository and represents the full version of the dataset used in the CAW 2.0 - Content Analysis for the WEB 2.0 workshop for the WWW 2009 conference that can be found in several repositories such as Konect/n/nBarrapunto conversations (Jan 2005 - Dec 2008)/nOnline conversations generated at Barrapunto (Spanish clone of Slashdot) during three years. For each discussion thread: sub-domains, title, topics and hierarchical relations between comments. For each comment: user, date, score and textual content Wikipedia (2001 - Mar 2010) Data from articles discussions (talk) pages of the English Wikipedia as of March 2010. It contains comments on about 870,000 articles (i.e. all articles which had a corresponding talk page with at least one comment), in total about 9.4 million comments. The oldest comments date back to as early as 2001.
Submitted by Marina LOSADA YÁÑEZ (marina.losada@upf.edu) on 2016-05-11T17:43:34Z/nNo. of bitstreams: 1/nREADME.txt: 3291 bytes, checksum: 411ec60c7ede8c42c27ec76a26221dd8 (MD5)
Made available in DSpace on 2016-05-11T17:43:34Z (GMT). No. of bitstreams: 1/nREADME.txt: 3291 bytes, checksum: 411ec60c7ede8c42c27ec76a26221dd8 (MD5)
eng
Informació addicional: http://www.mbfys.ru.nl/staff/v.gomez/
Publicació relacionada: Gómez V, Kaltenbrunner A, López V. Statistical analysis of the social network and discussion threads in Slashdot. In: WWW '08 Proceedings of the 17th international conference on World Wide Web; 2008 April 21-25; Beijing, China. New York: ACM; 2008. p. 645-54. DOI: 10.1145/1367497.1367585
Publicació relacionada: Gómez V, Kappen HJ, Litvak N, Kaltenbrunner A. A likelihood-based framework for the analysis of discussion threads. World Wide Web. 2013;16(5):645-75. DOI: 10.1007/s11280-012-0162-8. http://hdl.handle.net/10230/26746
Publicació relacionada: Laniado D, Tasso R, Volkovich Y, Kaltenbrunner A. When the Wikipedians talk: network and tree structure of Wikipedia discussion pages. In: Proceedings of the Fifth International Conference on Weblogs and Social Media (ICWSM-11); 2011 July 17-21; Barcelona, Spain. California: The AAAI Press, [2011]. p. 177-84. http://hdl.handle.net/10230/26817
http://hdl.handle.net/10230/26816
http://hdl.handle.net/10230/26746
http://hdl.handle.net/10230/26817
Aquest document està subjecte a una llicència Creative Commons
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
The Online conversation threads repository
info:eu-repo/semantics/other
Dataset
Online conversations
Discussion threads
Slashdot
Barrapunto
Wikipedia
oai:repositori.upf.edu:10230/270032016-11-18T11:36:52Zcom_10230_5963col_10230_24646
Rashid, Zulqarnain
2016-07-06T08:19:54Z
2016-07-06T08:19:54Z
2016-07-06
Rashid Z. Towards independent living of motor disabled people [dataset]. Repositori Digital de la UPF: Barcelona; 2016 [citat 7 juliol 2016]. Disponible a: http://hdl.handle.net/10803/350796
http://hdl.handle.net/10230/27003
The video shows different interaction methods, interfaces and representative motor disabled users. There are three categories of wheelchair users based on their degree of disability identified in this thesis. The designed interfaces and interaction methods are tested and validated by number of wheelchair users, some of them are shown in this video. They performed experiments and used our designed interfaces. Experiments include qualitative and quantitative evaluation. Based on the evaluation with the representative users we concluded the success of our research and interfaces.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2016-07-06T08:19:53Z/nNo. of bitstreams: 1/nInteraction Paradigm.mp4: 99308404 bytes, checksum: 59d65123f2da2d55dea4f7cfa8cde297 (MD5)
Made available in DSpace on 2016-07-06T08:19:54Z (GMT). No. of bitstreams: 1/nInteraction Paradigm.mp4: 99308404 bytes, checksum: 59d65123f2da2d55dea4f7cfa8cde297 (MD5)
eng
Publicació relacionada: Rashid Z. Cricking implementation with augmented reality and RFID: towards independent living of people with motor disabilities. 2016 http://hdl.handle.net/10230/25805
http://hdl.handle.net/10803/350796
Tots els drets reservats
info:eu-repo/semantics/openAccess
Towards independent living of motor disabled people
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/270212017-11-28T10:36:27Zcom_10230_5963col_10230_24646
Oramas, Sergio
Espinosa-Anke, Luis
Barcelona (Catalunya)
2016-07-11T09:59:27Z
2016-07-11T09:59:27Z
2016-01
Oramas S, Espinosa-Anke L. KBSF (Information Extraction for Knowledge Base Construction in the Music Domain) [dataset]. Repositori Digital de la UPF: Barcelona; 2016 [cited 2016 July 11]. Available from: http://hdl.handle.net/10230/27021
http://hdl.handle.net/10230/27021
JSON and HTML files. The dataset includes the 5 KBs generated plus the evaluation subsets used in the manual evaluation (top 100 and random 100). The best version of the dataset (the one with higher precision) is KBSF-th
Informació addicional: http://mtg.upf.edu/node/3526
Knowledge Base automatically extracted from songfacts.com following the methodology described in the following paper: Sergio Oramas, Luis Espinosa-Anke, Mohamed Sordo, Horacio Saggion, Xavier Serra, Information extraction for knowledge base construction in the music domain, Data & Knowledge Engineering, Available online 7 June 2016, ISSN 0169-023X, http://dx.doi.org/10.1016/j.datak.2016.06.001./nThe dataset includes the 5 KBs generated plus the evaluation subsets used in the manual evaluation (top 100 and random 100). The best version of the dataset (the one with higher precision) is KBSF-th. Further information about the dataset and the evaluation results can be found in the paper.
Submitted by Marina LOSADA YÁÑEZ (marina.losada@upf.edu) on 2016-07-11T09:59:27Z/nNo. of bitstreams: 3/nkbsf_dataset.zip: 34550163 bytes, checksum: 8ff2abb6127d25506a3f21440d0780cb (MD5)/nREADME.txt: 1135 bytes, checksum: f364c363a15a63a54d1fea94fdeec75d (MD5)/nlicense_rdf: 1370 bytes, checksum: ac86fe33011bda956ed9b032abf4d402 (MD5)
Made available in DSpace on 2016-07-11T09:59:27Z (GMT). No. of bitstreams: 3/nkbsf_dataset.zip: 34550163 bytes, checksum: 8ff2abb6127d25506a3f21440d0780cb (MD5)/nREADME.txt: 1135 bytes, checksum: f364c363a15a63a54d1fea94fdeec75d (MD5)/nlicense_rdf: 1370 bytes, checksum: ac86fe33011bda956ed9b032abf4d402 (MD5)/n Previous issue date: 2016-01
Funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Programme (MDM-2015-0502)
eng
Universitat Pompeu Fabra
Publicació relacionada: Oramas S, Espinosa-Anke L, Sordo M, Saggion H, Serra X. Information extraction for knowledge base construction in the music domain. Data Knowl Eng. 2016;106:70-83. DOI: 10.1016/j.datak.2016.06.001 http://hdl.handle.net/10230/33366
http://hdl.handle.net/10230/33366
Creative Commons CC-BY Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
KBSF (Information Extraction for Knowledge Base Construction in the Music Domain)
info:eu-repo/semantics/other
Dataset
Knowledge base
Music
Information extraction
oai:repositori.upf.edu:10230/274952018-11-15T10:53:59Zcom_10230_5963col_10230_24646
Oramas, Sergio
Ostuni, Vito Claudio
Vigliensoni, Gabriel
2016-11-11T11:04:33Z
2016-11-11T11:04:33Z
2016
Oramas S, Ostuni VC, Vigliensoni G. Sound and music recommendation with knowledge graphs [dataset]. Repositori Digital de la UPF: Barcelona; 2016 [citat 11 novembre 2016]. Disponible a: http://hdl.handle.net/10230/27495
http://hdl.handle.net/10230/27495
Music Recommendation Dataset (KGRec-music). Number of items: 8,640. Number of users: 5,199. Number of items-users interactions: 751,531. All the data comes from songfacts.com and last.fm websites. Items are songs, which are described in terms of textual description extracted from songfacts.com, and tags from last.fm. Files and folders in the dataset: /descriptions: In this folder there is one file per item with the textual description of the item. The name of the file is the id of the item plus the ".txt" extension. /tags: In this folder there is one file per item with the tags of the item separated by spaces. Multiword tags are separated by -. The name of the file is the id of the item plus the ".txt" extension. Not all items have tags, there are 401 items without tags. implicit_lf_dataset.txt: This file contains the interactions between users and items. There is one line per interaction (a user that downloaded a sound in this case) with the following format, fields in one line are separated by tabs: user_id /t sound_id /t 1 /n. Sound Recommendation Dataset (KGRec-sound). Number of items: 21,552. Number of users: 20,000. Number of items-users interactions: 2,117,698. All the data comes from Freesound.org. Items are sounds, which are described in terms of textual description and tags created by the sound creator at uploading time. Files and folders in the dataset: /descriptions: In this folder there is one file per item with the textual description of the item. The name of the file is the id of the item plus the ".txt" extension. /tags: In this folder there is one file per item with the tags of the item separated by spaces. The name of the file is the id of the item plus the ".txt" extension. downloads_fs_dataset.txt: This file contains the interactions between users and items. There is one line per interaction (a user that downloaded a sound in this case) with the following format, fields in one line are separated by tabs: /nuser_id /t sound_id /t 1 /n.
Two different datasets with users, items, implicit feedback interactions between users and items, item tags, and item text descriptions are provided, one for Music Recommendation (KGRec-music), and other for Sound Recommendation (KGRec-sound).
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2016-11-11T11:04:32Z/nNo. of bitstreams: 1/nKGRec-dataset.zip: 56553416 bytes, checksum: aedb66d1c2bbb9b65756ee7126e0e49b (MD5)
Made available in DSpace on 2016-11-11T11:04:33Z (GMT). No. of bitstreams: 1/nKGRec-dataset.zip: 56553416 bytes, checksum: aedb66d1c2bbb9b65756ee7126e0e49b (MD5)/n Previous issue date: 2016
eng
Publicació relacionada: Oramas S, Ostuni VC, Di Noia T, Serra X, Di Sciascio E. Sound and music recommendation with knowledge graphs. ACM Trans Intell Syst Technol. 2016; 8 (2): 21. DOI: 10.1145/2926718 http://hdl.handle.net/10230/35759
http://mtg.upf.edu/download/datasets/knowledge-graph-rec
http://hdl.handle.net/10230/35759
This dataset is licensed under Creative Commons CC BY-NC 3.0, except 3rd party data. Song text descriptions are licensed by Songfacts.com and user interactions and tags by Last.fm. Note that this dataset is considered derivative work according to paragraph 4.1 of Last.fm’s API Terms of Service. The data is made available for non-commercial use.
http://creativecommons.org/licenses/by-nc/3.0/
info:eu-repo/semantics/openAccess
Sound and music recommendation with knowledge graphs [dataset]
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/275292018-02-19T10:36:26Zcom_10230_5963col_10230_24646
CompMusic
2016-11-17T11:05:52Z
2016-11-17T11:05:52Z
2014
CompMusic. Beijing opera percussion instrument dataset [dataset]. Repositori Digital de la UPF: Barcelona; 2014 [citat 17 novembre 2016]. Disponible a: http://hdl.handle.net/10230/27529
http://hdl.handle.net/10230/27529
The Beijing Opera percussion instrument dataset is a collection of audio examples of individual strokes spanning the four percussion instrument classes used in Beijing Opera (Jingju, 京剧)./nBeijing Opera uses six main percussion instruments that can be grouped into four classes: /n/n1/ Bangu (Clapper-drum) consisting of Ban (the clapper, a wooden board-shaped instrument) + danpigu (a wooden drum struck by two wooden sticks)/n2/ Naobo (Cymbals) consisting of two cymbal instruments Qibo+Danao/n3/ Daluo: Large gong/n4/ Xiaoluo: Small gong/n/nAudio content:/nThe dataset provides audio examples for each of these instrument classes. /n/nThe audio examples were recorded under studio conditions by Mi Tian at the Centre for Digital Music, Queen Mary University of London, UK in September 2013 using an AKG C414 microphone. The audio was sampled at 44.1 kHz and stored as 16 bit wav files. The instruments were played by Ying Wan of the London Jing Kun Opera Association. Unlike some instruments that can be tuned, these percussion instruments are made from metal casting. Thus, there can be subtle timbral differences even across different instruments of the same kind. For each of these instruments, we used 2-3 individual instruments to record the samples, hoping to achieve a better timbre coverage. Further, audio samples were recorded using different playing techniques for each instrument. /n /nThe dataset can be used for training models for each percussion instrument class./n/nThe whole dataset (wav files) can be downloaded as a pack from Freesound: http://freesound.org/people/ajaysm/packs/14056/
Beijing Opera percussion dataset is a collection of 236 examples of isolated strokes spanning the four percussion instrument classes used in Beijing Opera. It can be used to build stroke models for each percussion instrument. /nAll the sounds in this pack were played by Ying Wan of the London Jing Kun Opera Association. Recorded by Mi Tian at the Centre for Digital Music, Queen Mary University of London, UK in September 2013 using an AKG C414 microphone under studio conditions.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2016-11-17T11:05:52Z/nNo. of bitstreams: 2/nBeijing Opera Percussion Instrument Dataset.docx: 23962 bytes, checksum: 7845718cf4fe96c7b5815b927d10c6e0 (MD5)/n14056__ajaysm__qmul-beijingoperapercussion.zip: 69426734 bytes, checksum: c03cb04dddcedb20a51c174f4ae63754 (MD5)
Made available in DSpace on 2016-11-17T11:05:52Z (GMT). No. of bitstreams: 2/nBeijing Opera Percussion Instrument Dataset.docx: 23962 bytes, checksum: 7845718cf4fe96c7b5815b927d10c6e0 (MD5)/n14056__ajaysm__qmul-beijingoperapercussion.zip: 69426734 bytes, checksum: c03cb04dddcedb20a51c174f4ae63754 (MD5)/n Previous issue date: 2016
eng
http://compmusic.upf.edu/bo-perc-dataset
Publicació relacionada: Tian M, Srinivasamurthy A, Sandler M, Serra X. A study of instrument-wise onset detection in Beijing Opera percussion ensembles. In: In: 2014 International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2014 May 4-9; Florence, Italy. Piscataway, NJ: IEEE, 2014. p. 2159-2163. DOI: 10.1109/ICASSP.2014.6853981 /nhttp://hdl.handle.net/10230/25678
Beijing Opera (京剧)
http://hdl.handle.net/10230/25678
info:eu-repo/grantAgreement/EC/FP7/267583
Creative Commons CC-BY Atribució
http://creativecommons.org/licenses/by/3.0/
info:eu-repo/semantics/openAccess
Beijing opera percussion instrument dataset
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/275412018-02-19T10:37:28Zcom_10230_5963col_10230_24646
CompMusic
2016-11-18T09:01:59Z
2016-11-18T09:01:59Z
2014
CompMusic. Turkish makam symbolic phrase dataset [dataset]. Repositori Digital de la UPF: Barcelona; 2014 [citat 17 novembre 2016]. Disponible a: http://hdl.handle.net/10230/27541
http://hdl.handle.net/10230/27541
The dataset consists of 31362 phrases on a set of 480 scores of different compositions annotated by 3 experts.
This study presents a large machine-readable dataset of Turkish makam music scores segmented into phrases by experts of this music. The segmentation facilitates computational research on melodic similarity between phrases, and relation between melodic phrasing and meter, rarely studied topics due to unavailability of data resources.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2016-11-18T09:01:59Z/nNo. of bitstreams: 1/notmm_symbolic_phrase_dataset-1.0.zip: 3858204 bytes, checksum: 3555f9068f6d2f0b0eeb532790ef7be9 (MD5)
Made available in DSpace on 2016-11-18T09:01:59Z (GMT). No. of bitstreams: 1/notmm_symbolic_phrase_dataset-1.0.zip: 3858204 bytes, checksum: 3555f9068f6d2f0b0eeb532790ef7be9 (MD5)/n Previous issue date: 2014
eng
Publicació relacionada: Karaosmanoglu MK, Bozkurt B, Holzapfel A, Disiacik ND. A symbolic dataset of Turkish makam music phrases. In: Holzapfel A, editors. Proceedings of the Fourth International Workshop on Folk Music Analysis (FMA2014); 2014 June 12-13; Istanbul, Turkey. Istanbul, Turkey: Computer Engineering Department, 2014. p. 10-14.
Ottoman-Turkish makam music
info:eu-repo/grantAgreement/EC/FP7/267583
Creative Commons CC-BY Atribució
http://creativecommons.org/licenses/by/3.0/
info:eu-repo/semantics/openAccess
Turkish makam symbolic phrase dataset
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/275622018-02-19T10:38:32Zcom_10230_5963col_10230_24646
CompMusic
2016-11-22T11:36:43Z
2016-11-22T11:36:43Z
2014
CompMusic. Turkish şarkı vocal dataset: version 1 [dataset]. Repositori Digital de la UPF: Barcelona; 2014 [citat 18 novembre 2016]. Disponible a: http://hdl.handle.net/10230/27562
http://hdl.handle.net/10230/27562
It features 10 performances of different compositions. Five are sung by male and 5 by female singer. The recordings are selected so that there is only a single main vocalist (or the intensity of the back vocalst is relatively low compared to the main vocalist). Accompanying instruments are mainly string ensembles. No percussive instruments are present. Audio is in .wav format./n- Lyrical phrases annotations-/n /nThe audio is segmented into one-section chunks (a section is nakarat, meyan etc.)/nEach audio segment is aligned to the lyrical phrases. A phrase corresponds roughly to a musical bar and contains 1 or 2 words. /n/nAn annotation file is in .TextGrid format of Praat.
Turkish şarkı vocal dataset is a collection of recordings of compositions from the vocal form şarkı. The recordings are selected from a musicBrainz collection of Turkish music: /n/nhttp://musicbrainz.org/collection/544f7aec-dba6-440c-943f-103cf344efbb. /n/nThe collection has annotations with lyrics. Each lyrical phrase is aligned to its corresponding segment in the audio.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2016-11-22T11:36:43Z/nNo. of bitstreams: 1/nturkish-makam-lyrics-2-audio-test-data-10-sarki_dataset.zip: 895819814 bytes, checksum: e81ba54c2c653f7124338b82d8a0fcbc (MD5)
Made available in DSpace on 2016-11-22T11:36:43Z (GMT). No. of bitstreams: 1/nturkish-makam-lyrics-2-audio-test-data-10-sarki_dataset.zip: 895819814 bytes, checksum: e81ba54c2c653f7124338b82d8a0fcbc (MD5)/n Previous issue date: 2014
eng
http://compmusic.upf.edu/turkish-sarki
Publicació relacionada: Dzhambazov G, Sentürk S, Serra X. Automatic lyrics-to-audio alignment in classical Turkish music. In: Holzapfel A, editors. Proceedings of the Fourth International Workshop on Folk Music Analysis (FMA2014); 2014 June 12-13; Istanbul, Turkey. Istanbul, Turkey: Computer Engineering Department, 2014. p. 61-64.
Versió 2: http://hdl.handle.net/10230/27563
Ottoman-Turkish makam music
info:eu-repo/grantAgreement/EC/FP7/267583
Creative Commons CC-BY Atribució
http://creativecommons.org/licenses/by/3.0/
info:eu-repo/semantics/openAccess
Turkish şarkı vocal dataset: version 1
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/275632018-02-19T10:39:16Zcom_10230_5963col_10230_24646
CompMusic
2016-11-22T12:11:58Z
2016-11-22T12:11:58Z
2015
CompMusic. Turkish şarkı vocal dataset: version 1 [dataset]. Repositori Digital de la UPF: Barcelona; 2015 [citat 18 novembre 2016]. Disponible a: http://hdl.handle.net/10230/27563
http://hdl.handle.net/10230/27563
It is an extension of Version 1 and features 12 performances of 11 different compositions./n- Lyrical phrases annotations-/n /nThe audio is segmented into one-section chunks (a section is nakarat, meyan etc.)/nEach audio segment is aligned to the lyrical phrases. A phrase corresponds roughly to a musical/nbar and contains 1 or 2 words. /n/nAn annotation file is in .TextGrid format of Praat.
Turkish şarkı vocal dataset is a collection of recordings of compositions from the vocal form şarkı. The recordings/nare selected from a musicBrainz collection of Turkish music/n/nhttp://musicbrainz.org/collection/544f7aec-dba6-440c-943f-103cf344efbb /n/nThe collection has annotations with lyrics. Each lyrical phrase is aligned to its corresponding segment in the audio.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2016-11-22T12:11:58Z/nNo. of bitstreams: 1/nturkish-makam-lyrics-2-audio-test-data-synthesis-2.0.zip: 613806424 bytes, checksum: d2cd87bcc87c15d109064f115817bdc3 (MD5)
Made available in DSpace on 2016-11-22T12:11:58Z (GMT). No. of bitstreams: 1/nturkish-makam-lyrics-2-audio-test-data-synthesis-2.0.zip: 613806424 bytes, checksum: d2cd87bcc87c15d109064f115817bdc3 (MD5)/n Previous issue date: 2015
eng
http://compmusic.upf.edu/turkish-sarki
Versió 1: http://hdl.handle.net/10230/27562
Publicació relacionada: Dzhambazov G, Serra X. Modeling of phoneme durations for alignment between polyphonic audio and lyrics. In: Timoney J, Lysaght T, editors. 12th Sound and Music Computing Conference; 2015 jul. 30-ag. 1; Maynooth (Ireland). Maynooth: Music Technology Research Group, Department of Computer Science, Maynooth University; 2015. Oral session 7, Computational musicology and mathematical music theory 1; p. 281-286. http://hdl.handle.net/10230/27614
Ottoman-Turkish makam music
http://hdl.handle.net/10230/27614
info:eu-repo/grantAgreement/EC/FP7/267583
Creative Commons CC-BY Atribució
http://creativecommons.org/licenses/by/3.0/
info:eu-repo/semantics/openAccess
Turkish şarkı vocal dataset: version 2
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/276062018-02-19T10:40:08Zcom_10230_5963col_10230_24646
CompMusic
2016-11-25T11:22:02Z
2016-11-25T11:22:02Z
2015
CompMusic. Turkish makam acapella sections dataset [dataset]. Repositori Digital de la UPF: Barcelona; 2015 [citat 25 novembre 2016]. Disponible a: http://hdl.handle.net/10230/27606
http://hdl.handle.net/10230/27606
Audio music content: /nThe collection has annotations with section, lyrics phrases and lyrics words. Each section, lyrics word and lyrical phrase is aligned to its corresponding segment in the audio. Annotations of secitons (aranağme, zemin etc.) are taken from https://github.com/MTG/turkish_makam_section_dataset/nFORMAT: All annotations in TextGrid (used in Praat)
Turkish makam acapella sections dataset is sung by professional singers and is a collection of recordings of compositions from the vocal form şarkı. They are selected to be the same as the recordings in version two of http://compmusic.upf.edu/turkish-sarki/nThe main intention is to provide acapella counterpart to polyphonic recordings.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2016-11-25T11:22:02Z/nNo. of bitstreams: 2/nturkish-makam-acapella-sections-dataset-2.0.zip: 292686676 bytes, checksum: 8f0514c03800df97f8e8dc0c10fc07cf (MD5)/nmakam_acapella-master.zip: 124403442 bytes, checksum: 90026cb837bb1df34e3ad0daf1f66c88 (MD5)
Made available in DSpace on 2016-11-25T11:22:02Z (GMT). No. of bitstreams: 2/nturkish-makam-acapella-sections-dataset-2.0.zip: 292686676 bytes, checksum: 8f0514c03800df97f8e8dc0c10fc07cf (MD5)/nmakam_acapella-master.zip: 124403442 bytes, checksum: 90026cb837bb1df34e3ad0daf1f66c88 (MD5)/n Previous issue date: 2015
eng
http://compmusic.upf.edu/turkish-makam-acapella-sections-dataset
Publicació relacionada: Dzhambazov G, Serra X. Modeling of phoneme durations for alignment between polyphonic audio and lyrics. In: Timoney J, Lysaght T, editors. 12th Sound and Music Computing Conference; 2015 jul. 30-ag. 1; Maynooth (Ireland). Maynooth: Music Technology Research Group, Department of Computer Science, Maynooth University; 2015. Oral session 7, Computational musicology and mathematical music theory 1; p. 281-286./nhttp://hdl.handle.net/10230/27614
Publicació relacionada: Dzhambazov G, Senturk S, Serra X. Searching lyrical phrases in a-capella Turkish Makam recordings. In: 16th International Society for Music Information Retrieval (ISMIR) Conference; 2015 Oct 26-30; Malaga, Spain.
Ottoman-Turkish makam music
http://hdl.handle.net/10230/27614
info:eu-repo/grantAgreement/EC/FP7/267583
The audio content and annotations are openly available for non-commercial research purposesfor non-commercial research purposes under the Attribution-NonCommercial-NonDerivs 3.0 Creative Commons license
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
Turkish makam acapella sections dataset
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/276072018-02-19T10:42:00Zcom_10230_5963col_10230_24646
CompMusic
2016-11-25T11:42:19Z
2016-11-25T11:42:19Z
2014
CompMusic. Turkish makam section dataset [dataset]. Repositori Digital de la UPF: Barcelona; 2014 [citat 25 novembre 2016]. Disponible a: http://hdl.handle.net/10230/27607
http://hdl.handle.net/10230/27607
http://dx.doi.org/10.5281/zenodo.168210
This release contains 2095 sections annotated in 257 audio recordings of 58 compositions. The midi and SymbTr-scores of the compositions are also included in the dataset.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2016-11-25T11:42:19Z/nNo. of bitstreams: 1/notmm_section_dataset-2014_jnmr.zip: 3036183 bytes, checksum: 7fa7930c2f8411d9c5460e3156ac92c8 (MD5)
Made available in DSpace on 2016-11-25T11:42:19Z (GMT). No. of bitstreams: 1/notmm_section_dataset-2014_jnmr.zip: 3036183 bytes, checksum: 7fa7930c2f8411d9c5460e3156ac92c8 (MD5)/n Previous issue date: 2014
eng
Publicació relacionada: Şentürk S, Holzapfel A, Serra X. Linking scores and audio recordings in makam music of Turkey. Journal of New Music Research. 2014;43:34–52. DOI: 10.1080/09298215.2013.864681 http://hdl.handle.net/10230/25698
http://compmusic.upf.edu/node/234
Ottoman-Turkish makam music
http://hdl.handle.net/10230/25698
info:eu-repo/grantAgreement/EC/FP7/267583
This dataset is licensed under a Creative Commons Attribution 4.0 International License
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Turkish makam section dataset
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/276182018-02-19T10:42:44Zcom_10230_5963col_10230_24646
CompMusic
2016-11-28T11:48:11Z
2016-11-28T11:48:11Z
2013
CompMusic. Turkish makam tonic test dataset v1 [dataset]. Repositori Digital de la UPF: Barcelona; 2013 [citat 28 novembre 2016]. Disponible a: http://hdl.handle.net/10230/27618
http://hdl.handle.net/10230/27618
This release contains annotated tonic frequencies of 257 audio recordings. The SymbTr-scores of the corresponding compositions performed in the audio recordings are also indicated.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2016-11-28T11:48:11Z/nNo. of bitstreams: 1/notmm_tonic_dataset-2013_ismir.zip: 344915 bytes, checksum: 5fe44cc5801565d46d626137f4db237c (MD5)
Made available in DSpace on 2016-11-28T11:48:11Z (GMT). No. of bitstreams: 1/notmm_tonic_dataset-2013_ismir.zip: 344915 bytes, checksum: 5fe44cc5801565d46d626137f4db237c (MD5)/n Previous issue date: 2013
eng
https://github.com/MTG/otmm_tonic_dataset/releases
Publicació relacionada: Şentürk S, Gulati S, Serra X. Score informed tonic identification for Makam music of Turkey. Proceedings of 14th International Society for Music Information Retrieval Conference. Curitiba, Brazil; 4-8 novembre de 2013./nhttp://hdl.handle.net/10230/27728
Versió 2: http://hdl.handle.net/10230/27750; Versió 3: http://hdl.handle.net/10230/27772
Ottoman-Turkish makam music
http://hdl.handle.net/10230/27728
info:eu-repo/grantAgreement/EC/FP7/267583
Licensed under a Creative Commons Attribution 4.0 International License
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Turkish makam tonic test dataset v1
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/277502018-02-19T10:43:17Zcom_10230_5963col_10230_24646
CompMusic
2016-12-13T12:05:30Z
2016-12-13T12:05:30Z
2015
CompMusic. Turkish makam tonic test dataset v2 [dataset]. Repositori Digital de la UPF: Barcelona; 2015 [citat 13 desembre 2016]. Disponible a: http://hdl.handle.net/10230/27750
http://hdl.handle.net/10230/27750
This release adds the test datasets consisting the annotated tonic frequencies of audio recordings of Turkish-makam music used in the paper:/nAtlı, H. S., Bozkurt, B., Şentürk, S. (2015). A Method for Tonic Frequency Identification of Turkish Makam Music Recordings. In Proceedings of 5th International Workshop on Folk Music Analysis. Paris, France./n/nThe "source code" also includes the previous releases. Download the zip file (atli2015tonic_fma.zip) to specifically download the datasets used in the paper above.
This release contains annotated tonic frequencies of 1093 audio recordings in total.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2016-12-13T12:05:30Z/nNo. of bitstreams: 2/natli2015tonic_fma.zip: 295042 bytes, checksum: c6f988fafc20cd16f1b27b374ef2a502 (MD5)/notmm_tonic_dataset-2015_fma.zip: 638464 bytes, checksum: 5f4fa6142f4969d8985a051fb5c8c9ef (MD5)
Made available in DSpace on 2016-12-13T12:05:30Z (GMT). No. of bitstreams: 2/natli2015tonic_fma.zip: 295042 bytes, checksum: c6f988fafc20cd16f1b27b374ef2a502 (MD5)/notmm_tonic_dataset-2015_fma.zip: 638464 bytes, checksum: 5f4fa6142f4969d8985a051fb5c8c9ef (MD5)/n Previous issue date: 2015
eng
Versió 1: http://hdl.handle.net/10230/27618; Versió 3: http://hdl.handle.net/10230/27772
Publicació relacionada: Ath HS, Bozkurt B, Şentürk S. A method for tonic frequency identification of Turkish Makam music recordings. In: 5th International Workshop on Folk Music Analysis; 2015 Jun 10-12; Paris, France. [place unknown]: Association Dirac; 2015. p. 119-22. http://hdl.handle.net/10230/32132
Ottoman-Turkish makam music
http://hdl.handle.net/10230/32132
https://github.com/MTG/otmm_tonic_dataset/releases
info:eu-repo/grantAgreement/EC/FP7/267583
Licensed under a Creative Commons Attribution 4.0 International License
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Turkish makam tonic test dataset v2
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/277722018-11-22T12:37:10Zcom_10230_5963col_10230_24646
CompMusic
2016-12-15T09:55:25Z
2016-12-15T09:55:25Z
2015
CompMusic. OTMM Tonic Test Dataset v3.0.0 [dataset]. Repositori Digital de la UPF: Barcelona; 2015 [citat 15 desembre 2016]. Disponible a: http://hdl.handle.net/10230/27772
http://hdl.handle.net/10230/27772
Each annotated recording is uniquely identified with a MusicBrainz MBID. The tonic symbol is also for each recording given in the format [letter][octave][accidental][comma], e.g. B4b1 (according to AEU theory)./n/nEach recording is annotated by at least expert musician or musicologists. The annotations are stored as a list with each annotation including the annotated frequency, source dataset, relevant publication, additional observations written by the annotator and whether the octave of the annotated value is considered (for example, the octave is ambiguous in orchestral instrumental recordings)./nThe data is stored as JSON file and organized as a dictionary of recordings. An example recording is displayed below:/n/n```json/n{/n "mbid": "http://musicbrainz.org/recording/e3a22684-d237-48b5-ac27-e9b77ddd3c18", /n "verified": true, /n "annotations": [/n {/n "source": "https://github.com/MTG/otmm_tonic_dataset/blob/7f28c1a3261b9146042155ee5e0f9e644d9ebcfa/senturk2013karar_ismir/tonic_annotations.csv", /n "citation": "Şentürk, S., Gulati, S., and Serra, X. (2013). Score Informed Tonic Identification for Makam Music of Turkey. In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR 2013), pages 175–180, Curitiba, Brazil.", /n "octave_wrapped": true, /n "observations": "", /n "value": 296.9597/n }, /n {/n "source": "https://github.com/MTG/otmm_tonic_dataset/blob/7f28c1a3261b9146042155ee5e0f9e644d9ebcfa/atli2015tonic_fma/TD2.csv", /n "citation": "Atlı, H. S., Bozkurt, B., Şentürk, S. (2015). A Method for Tonic Frequency Identification of Turkish Makam Music Recordings. In Proceedings of 5th International Workshop on Folk Music Analysis (FMA 2015), pages 119–122, Paris, France.", /n "octave_wrapped": true, /n "observations": "", /n "value": 296/n }/n ], /n "tonic_symbol": "D4"/n}/n```/n/n/nMost of the recordings in this dataset cannot be shared due to copyright. However relevant features are already computed and they can be downloaded from the [Dunya-makam](dunya.compmusic.upf.edu/makam) after registration. Please refer to the API documentation (http://dunya.compmusic.upf.edu/docs/makam.html) to how to access the data.
This repository contains datasets of annotated tonic frequencies of the audio recordings of Ottoman-Turkish makam music. The annotations are compiled from several research papers published under the CompMusic project. For more information about the original datasets, please refer to the relevant paper.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2016-12-15T09:55:24Z/nNo. of bitstreams: 1/notmm_tonic_dataset-senturk2016thesis.zip: 83731 bytes, checksum: 164c19c729f9fbf99332209b490653a6 (MD5)
Made available in DSpace on 2016-12-15T09:55:25Z (GMT). No. of bitstreams: 1/notmm_tonic_dataset-senturk2016thesis.zip: 83731 bytes, checksum: 164c19c729f9fbf99332209b490653a6 (MD5)/n Previous issue date: 2015
eng
Publicació relacionada: Şentürk S, Gulati S, Serra X. Score informed tonic identification for Makam music of Turkey. Proceedings of 14th International Society for Music Information Retrieval Conference. Curitiba, Brazil; 4-8 novembre de 2013. http://hdl.handle.net/10230/27728
Versió 1: http://hdl.handle.net/10230/27618 ;Versió 2: http://hdl.handle.net/10230/27750
Ottoman-Turkish makam music
http://hdl.handle.net/10230/27728
https://github.com/MTG/otmm_tonic_dataset/releases
info:eu-repo/grantAgreement/EC/FP7/267583
Licensed under a Creative Commons Attribution 4.0 International License.
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
OTMM Tonic Test Dataset v3.0.0
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/277742018-02-19T10:44:33Zcom_10230_5963col_10230_24646
CompMusic
2016-12-15T10:39:43Z
2016-12-15T10:39:43Z
2014
CompMusic.Turkish makam melodic phrase dataset [dataset]. Repositori Digital de la UPF: Barcelona; 2014 [citat 15 desembre 2016]. Disponible a: http://hdl.handle.net/10230/27774
http://hdl.handle.net/10230/27774
There are 899 SymbTr-scores. The scores were manually annotated into melodic segments by 3 experts. In total, there are 31362 phrase annotations in this dataset.
Karaosmanoğlu and Bozkurt have studied the problem of usul and makam driven automatic melodic segmentation for Turkish Music.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2016-12-15T10:39:43Z/nNo. of bitstreams: 10/nuzman1_symbtr_txt.zip: 2045482 bytes, checksum: 32930589475414ccd83a338243c72ee0 (MD5)/nuzman2_symbtr_txt.zip: 854385 bytes, checksum: ecafd1ea809c80082cbbf1131d8a6112 (MD5)/nuzman3_symbtr_txt.zip: 876986 bytes, checksum: c82c3d6b83ae9562e491c1522773ec1f (MD5)/n5eser_pdf.rar: 14349419 bytes, checksum: 7b36f427ca5c3b26488fc3e39657f720 (MD5)/n5eser_symbtr.rar: 3058923 bytes, checksum: d6b9c8d2bc226e255a801ee14d1a8e9b (MD5)/nmid_500_1.rar: 1003727 bytes, checksum: 48c7780644a90d4bd7b467dd0ef76fde (MD5)/nmid_500_2.rar: 236980 bytes, checksum: 7101eb66c2895fb85c74d10c21aee7d6 (MD5)/nmatlabToolsForAutoSeg.zip: 347507 bytes, checksum: c21c13244b5f70cb82edd89b42c1f184 (MD5)/ndistributions_Data_Figures.zip: 241733 bytes, checksum: 93da6d7bb564217f612dc7d0c9f78bfa (MD5)/ninformation.txt: 1477 bytes, checksum: 40a82a1a543a705ba3aed767b8f1e310 (MD5)
Made available in DSpace on 2016-12-15T10:39:43Z (GMT). No. of bitstreams: 10/nuzman1_symbtr_txt.zip: 2045482 bytes, checksum: 32930589475414ccd83a338243c72ee0 (MD5)/nuzman2_symbtr_txt.zip: 854385 bytes, checksum: ecafd1ea809c80082cbbf1131d8a6112 (MD5)/nuzman3_symbtr_txt.zip: 876986 bytes, checksum: c82c3d6b83ae9562e491c1522773ec1f (MD5)/n5eser_pdf.rar: 14349419 bytes, checksum: 7b36f427ca5c3b26488fc3e39657f720 (MD5)/n5eser_symbtr.rar: 3058923 bytes, checksum: d6b9c8d2bc226e255a801ee14d1a8e9b (MD5)/nmid_500_1.rar: 1003727 bytes, checksum: 48c7780644a90d4bd7b467dd0ef76fde (MD5)/nmid_500_2.rar: 236980 bytes, checksum: 7101eb66c2895fb85c74d10c21aee7d6 (MD5)/nmatlabToolsForAutoSeg.zip: 347507 bytes, checksum: c21c13244b5f70cb82edd89b42c1f184 (MD5)/ndistributions_Data_Figures.zip: 241733 bytes, checksum: 93da6d7bb564217f612dc7d0c9f78bfa (MD5)/ninformation.txt: 1477 bytes, checksum: 40a82a1a543a705ba3aed767b8f1e310 (MD5)/n Previous issue date: 2014
eng
Ottoman-Turkish Makam Music
http://akademik.bahcesehir.edu.tr/bbozkurt/112E162_en.html
info:eu-repo/grantAgreement/EC/FP7/267583
Aquest document està subjecte a una llicència Creative Commons
http://creativecommons.org/licenses/by/3.0/
info:eu-repo/semantics/openAccess
Turkish makam melodic phrase dataset
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/277752018-02-19T10:47:41Zcom_10230_5963col_10230_24646
CompMusic
2016-12-15T11:25:10Z
2016-12-15T11:25:10Z
2014
CompMusic. Turkish makam music audio-score alignment dataset [dataset]. Repositori digital de la UPF: Barcelona; 2014 [citat 15 desembre 2016]. Disponible a: http://hdl.handle.net/10230/27775
http://hdl.handle.net/10230/27775
This release contains 6 audio recordings of 4 peşrev compositions from the classical Ottoman-Turkish tradition. There are 51 sections in the audio recordings in total. The total number of the note annotations in the audio recordings are 3896. These annotations typically follow the note sequence in the symbTr. There are 3 inserted and 49 omitted notes in the annotations with respect to the symbTr-scores./n/nThe dataset in this release is derived from the transcription test dataset used in the paper:/n /nBenetos, E. & Holzapfel, A. (2013). Automatic transcription of Turkish makam music. In Proceedings of 14th International Society for Music Information Retrieval Conference, 4 - 8 Nov 2013, Curitiba, PR, Brazil./n /nThe scores for each composition are obtained from the SymbTr collection explained in:/n /nKaraosmanoğlu, K. (2012). A Turkish makam music symbolic database for music information retrieval: SymbTr. In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), pages 223–228./n /nhttps://github.com/sertansenturk/turkish_makam_audio_score_alignment_dataset/release/n/n/nFrom the annotated score onsets for some of the above recordings only the main singing voice segments/nhave been selected. Further separately only a subset of vocal onsets crresponding to phoneme transitions rules have been explicitly annotated as annotationOnsets.txt/n /nPlease cite:/nDzhambazov, G., Srinivasamurthy A., Şentürk S., & Serra X. (2016). On the Use of Note Onsets for Improved Lyrics-to-audio Alignment in Turkish Makam Music. 17th International Society for Music Information Retrieval Conference (ISMIR 2016)/n /nhttps://github.com/MTG/otmm_audio_score_alignment_dataset/tree/vocal-only-annotation
The repository includes the test datasets used in various audio-score alignment experiments on Ottoman-Turkish makam music. /nThis particular release contains the audio-score alignment test dataset used in the paper: Şentürk, S., Gulati, S., and Serra, X. (2014). Towards alignment of score and audio recordings of Ottoman-Turkish makam music. In Proceedings of 4th International Workshop on Folk Music Analysis, pages 57–60, Istanbul, Turkey.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2016-12-15T11:25:10Z/nNo. of bitstreams: 1/notmm_audio_score_alignment_dataset-2014_fma.zip: 537997 bytes, checksum: 312f45116e9b35b688c6ab7d6c684af6 (MD5)
Made available in DSpace on 2016-12-15T11:25:10Z (GMT). No. of bitstreams: 1/notmm_audio_score_alignment_dataset-2014_fma.zip: 537997 bytes, checksum: 312f45116e9b35b688c6ab7d6c684af6 (MD5)/n Previous issue date: 2014
eng
Publicació relacionada: Sentürk S, Gulati S, Serra X. Towards alignment of score and audio recordings of ottoman-turkish makam music. In: Holzapfel A, editor. Proceedings of the Fourth International Workshop on Folk Music Analysis (FMA2014); 2014 June 12-13; Istanbul, Turkey. Istanbul (Turkey): Computer Engineering Department, Bogaziçi University; 2014. p. 57-60. http://hdl.handle.net/10230/32138
Ottoman-Turkish Makam Music
http://hdl.handle.net/10230/32138
Licensed under a Creative Commons Attribution 4.0 International License
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Turkish makam music audio-score alignment dataset
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/279562018-02-19T10:50:03Zcom_10230_5963col_10230_24646
CompMusic
2017-01-23T11:39:57Z
2017-01-23T11:39:57Z
2014
CompMusic. Carnatic varnam dataset [dataset]. Repositori Digital de la UPF: Barcelona; 2014 [citat 23 gener 2017]. Disponible a: http://hdl.handle.net/10230/27956
http://hdl.handle.net/10230/27956
Audio music content-----/nThey feature 7 varnams in 7 rāgas sung by 5 young professional singers who received training for more than 15 years. They are all set to Adi taala. Measuring the intonation variations require absolutely clean pitch contours. For this, all the varṇaṁs are recorded without accompanying instruments, except the drone./nTaala annotations-----/nThe recordings are annotated with taala cycles, each annotation marking the starting of a cycle. We have later automatically divided each cycle into 8 equal parts. The annotations are made available as sonic visualizer annotation layers. Each annotation is of the format m.n where m is the cycle number and n is the division within the cycle. All m.1 annotations are manually done, whereas m.[2-8] are automatically labelled./n/nNotations-----/nThe notations for 7 varnams are procured from an archive curated by Shivkumar, in word document format. They are manually converted to a machine readable format (yaml). Each file is essentially a dictionary with section names of the composition as keys. Each section is represented as a list of cycles. Each cycle in turn has a list of divisions./n/nPossible uses of the dataset-----/nThe distinct advantage of this dataset is the free availability of the audio content. Along with the annotations, it can be used for melodic analyses: characterizing intonation, motif discovery and tonic identification. The availability of a machine readable notation files allows the dataset to be used for audio-score alignment.
Carnatic varnam dataset is a collection of 28 solo vocal recordings, recorded for our research on intonation analysis of Carnatic raagas. The collection has the audio recordings, taala cycle annotations and notations in a machine readable format.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2017-01-23T11:39:57Z/nNo. of bitstreams: 2/nvarnam_notations_annotations.tgz: 140941 bytes, checksum: b7de7ceefdded7ce5548afc486ebdc6b (MD5)/n14136__gopalkoduri__carnatic-varnams.zip: 249529080 bytes, checksum: 0288bc0e0d75cfe4c618baff0a792b1e (MD5)
Made available in DSpace on 2017-01-23T11:39:57Z (GMT). No. of bitstreams: 2/nvarnam_notations_annotations.tgz: 140941 bytes, checksum: b7de7ceefdded7ce5548afc486ebdc6b (MD5)/n14136__gopalkoduri__carnatic-varnams.zip: 249529080 bytes, checksum: 0288bc0e0d75cfe4c618baff0a792b1e (MD5)/n Previous issue date: 2014
eng
Publicació relacionada: Koduri GK, Ishwar V, Serrà J, Serra X. Intonation analysis of rāgas in Carnatic music. Journal of New Music Research. 2014;43(01):73–94. DOI: 10.1080/09298215.2013.866145 /nhttp://hdl.handle.net/10230/25676
Indian art music
http://hdl.handle.net/10230/25676
http://compmusic.upf.edu/carnatic-varnam-dataset
info:eu-repo/grantAgreement/EC/FP7/267583
Aquest document està subjecte a una llicència Creative Commons
http://creativecommons.org/licenses/by/3.0/
info:eu-repo/semantics/openAccess
Carnatic varnam dataset
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/279362018-02-19T10:49:37Zcom_10230_5963col_10230_24646
CompMusic
2017-01-19T08:54:15Z
2017-01-19T08:54:15Z
2014-03
CompMusic.Mridangam stroke dataset [dataset]. Repositori Digital de la UPF: Barcelona; 2014 [citat 19 gener 2017]. Disponible a: http://hdl.handle.net/10230/27936
http://hdl.handle.net/10230/27936
The audio examples were recorded from a professional Carnatic percussionist in a semi-anechoic studio conditions by Akshay Anantapadmanabhan using SM-58 microphones and an H4n ZOOM recorder. The audio was sampled at 44.1 kHz and stored as 16 bit wav files. The dataset can be used for training models for each Mridangam stroke. /n/nA detailed description of the Mridangam and its strokes can be found in the paper below. A part of the dataset was used in the following paper. /nAkshay Anantapadmanabhan, Ashwin Bellur, Hema A. Murthy, "Modal analysis and transcription of strokes of the mridangam using non-negative matrix factorization," in Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), pp.181-185, May 2013./n/nThe whole dataset is organized by the tonic, into 6 packs in Freesound. The packs can be downloaded from the links below. /n/nB: http://www.freesound.org/people/akshaylaya/packs/14157//n/nC: http://www.freesound.org/people/akshaylaya/packs/14159//n/nC#: http://www.freesound.org/people/akshaylaya/packs/14160//n/nD: http://www.freesound.org/people/akshaylaya/packs/14161//n/nD#: http://www.freesound.org/people/akshaylaya/packs/14163//n/nE: http://www.freesound.org/people/akshaylaya/packs/14164//n/nEach audio file is named as, /n/n<StrokeName>_<Tonic>_<InstanceNumber>.wav/n<Tonic> = {B, C, Csh, D, Dsh, E}/n<StrokeName> = {Bheem, Cha, Dheem, Dhin, Num, Ta, Tha, Tham, Thi, Thom}
The Mridangam Stroke dataset is a collection of 7162 audio examples of individual strokes of the Mridangam in various tonics. The dataset comprises of 10 different strokes played on Mridangams with 6 different tonic values. The dataset can be used for training models for each Mridangam stroke.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2017-01-19T08:54:15Z/nNo. of bitstreams: 6/n14157__akshaylaya__compmusic-iitm-mridangam-stroke-dataset-b.zip: 37308940 bytes, checksum: 9824fe4de2028d69d186a9d3fba6444b (MD5)/n14159__akshaylaya__compmusic-iitm-mridangam-stroke-dataset-c.zip: 32888544 bytes, checksum: 15311b7c6ba0ca48fdfad22e053299b7 (MD5)/n14160__akshaylaya__compmusic-iitm-mridangam-stroke-dataset-c.zip: 32512778 bytes, checksum: 3392ac076fe777d5342258c3c42d20dd (MD5)/n14161__akshaylaya__compmusic-iitm-mridangam-stroke-dataset-d.zip: 26780449 bytes, checksum: c7f3aa9f8e07d2c4847b418f6e4b18d8 (MD5)/n14163__akshaylaya__compmusic-iitm-mridangam-stroke-dataset-d.zip: 56927322 bytes, checksum: f7a8a9d88803a7d950231fc09204f9b7 (MD5)/n14164__akshaylaya__compmusic-iitm-mridangam-stroke-dataset-e.zip: 38434706 bytes, checksum: 3ebda77248e6aa226ef951b873fa99ff (MD5)
Made available in DSpace on 2017-01-19T08:54:15Z (GMT). No. of bitstreams: 6/n14157__akshaylaya__compmusic-iitm-mridangam-stroke-dataset-b.zip: 37308940 bytes, checksum: 9824fe4de2028d69d186a9d3fba6444b (MD5)/n14159__akshaylaya__compmusic-iitm-mridangam-stroke-dataset-c.zip: 32888544 bytes, checksum: 15311b7c6ba0ca48fdfad22e053299b7 (MD5)/n14160__akshaylaya__compmusic-iitm-mridangam-stroke-dataset-c.zip: 32512778 bytes, checksum: 3392ac076fe777d5342258c3c42d20dd (MD5)/n14161__akshaylaya__compmusic-iitm-mridangam-stroke-dataset-d.zip: 26780449 bytes, checksum: c7f3aa9f8e07d2c4847b418f6e4b18d8 (MD5)/n14163__akshaylaya__compmusic-iitm-mridangam-stroke-dataset-d.zip: 56927322 bytes, checksum: f7a8a9d88803a7d950231fc09204f9b7 (MD5)/n14164__akshaylaya__compmusic-iitm-mridangam-stroke-dataset-e.zip: 38434706 bytes, checksum: 3ebda77248e6aa226ef951b873fa99ff (MD5)/n Previous issue date: 2014-03
eng
Publicació relacionada: Anantapadmanabhan A, Bellur A, Murthy HA. Modal analysis and transcription of strokes of the mridangam using non-negative matrix factorization. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing: proceedings; 2013 May 26-31; Vancouver, USA. Piscataway, NJ: IEEE, 2013. p. 181-185. DOI 10.1109/ICASSP.2013.6637633/nhttp://hdl.handle.net/10230/25756
Indian art music
http://hdl.handle.net/10230/25756
http://compmusic.upf.edu/mridangam-stroke-dataset
info:eu-repo/grantAgreement/EC/FP7/267583
Aquest document està subjecte a una llicència Creative Commons
http://creativecommons.org/licenses/by/3.0/
info:eu-repo/semantics/openAccess
Mridangam stroke dataset
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/279702018-02-19T10:50:28Zcom_10230_5963col_10230_24646
CompMusic
2017-01-24T12:47:49Z
2017-01-24T12:47:49Z
2014
CompMusic. Indian art music tonic datasets [dataset]. Repositori Digital de la UPF: Barcelona; 2014 [citat 24 gener 2017]. Disponible a: http://hdl.handle.net/10230/27970
http://hdl.handle.net/10230/27970
These datasets comprise audio excerpts and manually done annotations of the tonic pitch of the lead artist for each audio excerpt. Each excerpt is accompanied by its associated editorial metadata. These datasets can be used to develop and evaluate computational approaches for automatic tonic identification in Indian art music. These datasets have been used in several articles mentioned below. A majority of these datasets come from the CompMusic corpora of Indian art music, for which each recording is associated with a MBID. With the MBID other information can be obtained using the Dunya API. We here provide an overview of the tonic identification datasets. /nDatasets -------/nThe statistics about the datasets for tonic identification is listed in the table below. These six datasets are used in Gulati, S., Bellur, A., Salamon, J., Ranjani, H. G., Ishwar, V., Murthy, H. A., & Serra, X. (2014). Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation. Journal of New Music Research, 43(01), 55–73 for a comparative evaluation. To the best of our knowledge these are the largest datasets available for tonic identification for Indian art music. These datases vary in terms of the audio quality, recording period (decade), the number of recordings for Carnatic, Hindustani, male and female singers and instrumental and vocal excerpts. For a detailed information about these datasets we refer to Chapter 3 of this thesis (http://hdl.handle.net/10803/398984)./n/nThe audio files corresponding to these datsets are made available on request for only research purposes. To obtain the files fill the FORM (https://goo.gl/forms/kWzpCsZW8DM7noW63)./n/n---CompMusic Tonic Identification Datasets ---/n/nDatasets: CM1, CM2, CM3/n/nFeatures: pitch + multipitch histogram + pitch histograms/n/n /n/n---IITM Tonic Identification Datasets ---/n/nDatasets: IITM1, IITM2/n/nFeatures: pitch + multipitch histogram + pitch histograms/n/n /n/n--- IISc Tonic identification Dataset ---/n/nDataset: IISc/n/nFeatures: pitch + multipitch histogram + pitch histograms/n/n /n/nAnnotation Format ---/n/nThe tonic annotations are availabe both in tsv and json format. /n/nTSV: <relative path to audio><tab><tonic(Hz)><tab><Carnatic or Hindustani><tab><artist_name><tab><gender of the singer><vocal or instrumental> /n/nJSON: {/n 'artist': <name of the lead artist if available>, /n/n 'filepath': <relative path to the audio file>,/n/n 'gender': <gender of the lead singer if available>,/n/n 'mbid': <musicbrainz id when available>,/n/n 'tonic': <tonic in Hz>,/n/n 'tradition': <Hindustani or Carnatic>,/n/n 'type': <vocal or instrumental>/n }/n/n/nwhere keys of the main dictionary are the filepaths to the audio files (feature path is exactly the same with a different extension of the file name).
This dataset comprises 597 commercially available audio music recordings of Indian art music (Hindustani and Carnatic music), each manually annotated with the tonic of the lead artist. This dataset is used as the test corpus for the development of tonic identification approaches.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2017-01-24T12:47:49Z/nNo. of bitstreams: 3/nCompMusic Tonic Identification Datasets.rar: 662659586 bytes, checksum: 53e153f68c4a0f51d3f8747ca4084fcc (MD5)/nIITM Tonic Identification Datasets.rar: 390001791 bytes, checksum: 1894b5d49ced611d663e509c7d60d92d (MD5)/nIISc Tonic identification Dataset.rar: 58102391 bytes, checksum: 78fd0adcda5a4135c4e50ade4236a789 (MD5)
Made available in DSpace on 2017-01-24T12:47:49Z (GMT). No. of bitstreams: 3/nCompMusic Tonic Identification Datasets.rar: 662659586 bytes, checksum: 53e153f68c4a0f51d3f8747ca4084fcc (MD5)/nIITM Tonic Identification Datasets.rar: 390001791 bytes, checksum: 1894b5d49ced611d663e509c7d60d92d (MD5)/nIISc Tonic identification Dataset.rar: 58102391 bytes, checksum: 78fd0adcda5a4135c4e50ade4236a789 (MD5)/n Previous issue date: 2014
eng
Publicació relacionada: Gulati S, Bellur A, Salamon J, Ranjani HG, Ishwar V, Murthy HA, Serra X. Automatic tonic identification in Indian art music: approaches and evaluation. Journal of New Music Research. 2014; 43(1): 55–73. DOI: 10.1080/09298215.2013.875042 http://hdl.handle.net/10230/25675
Indian art music
http://hdl.handle.net/10230/25675
http://compmusic.upf.edu/iam-tonic-dataset
info:eu-repo/grantAgreement/EC/FP7/267583
Aquest document està subjecte a una llicència Creative Commons
http://creativecommons.org/licenses/by/3.0/
info:eu-repo/semantics/openAccess
Indian art music tonic datasets
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/282702020-09-10T06:10:38Zcom_10230_5963col_10230_24646
Maestre Gómez, Esteban
Marchini, Marco, 1984-
Papiotis, Panagiotis, 1985-
Pérez Carrillo, Alfonso Antonio, 1977-
2017-03-21T12:19:12Z
2017-03-21T12:19:12Z
2014
Maestre E, Marchini M, Papiotis P, Perez A. Ensemble Expressive Performance Dataset (EEP) [dataset]. Repositori Digital de la UPF: Barcelona; 2014 [cited 2017 March 21]. Available from: http://hdl.handle.net/10230/28270
http://hdl.handle.net/10230/28270
The dataset contains five extracts from Beethoven’s Concerto N.4, Op. 18:
(I) Allegro-Prestissimo movement,
(P.1) Allegro ma non tanto movement (bars 54-78),
(P.2) Allegro ma non tanto movement (bars 138-151),
(P.3) Menuetto (bars 8-50),
(P.4) Allegro-Prestissimo (bars 28-45).
The piece (I) was recorded 3 times with increasing degrees of expressiveness:
-Mechanical,
-Normal interpreted,
-Exaggerated.
The pieces (P.1-P.4) were recorded 5 times each:
-Ensemble performance,
-Solo performance of Violin 1,
-Solo performance of Violin 2,
-Solo performance of Viola,
-Solo performance of Cello,
The musicians have given their explicit approval for this dataset to be made public.
Audio Files:
Each recording contains audio tracks in the "Ambient Audio" node: a cardioid microphone and a binaural microphone (this file is best experienced with headphones). In addition, each recording includes contact microphone recording of each musician in separate tracks. These audios are included in the "Pickup Audio" node.
Score alignment:
We place a score-performance alignment file per musician in the "Score Alignment" node of each datapack. The original score by Beethoven can be retrieved in various formats here.
If downloaded, the score alignment file can be opened with a text editor and is written in human readable format. It contains, for each performed note a line like the following:
onset-time-in-secs offset-time-in-secs note-pitch
Polhemus MoCap Data:
Bowing motion descriptors can be found in the "Instrument Gestures" node of each datapack. Within this node the bowing descriptors relative to each musicians are places on separate nodes.
The raw Polhemus mocap data is found on the "Polhemus MoCap data" node and can be visualized by dragging such node to the first pane of in repovizz.
The dataset contains 23 multimodal recordings of string quartet performance. Acquired data includes the audio of each performance in separate tracks: up to two ambient tracks and individual tracks acquired through contact microphones. In addition each recording includes instrumental motion capture and a set of derived instrumental bowing descriptors for each musician. The recordings were made as part of the experiments on ensemble expressive performance.
Submitted by Dades de Recerca (repositori-becari3@upf.edu) on 2017-03-21T12:19:12Z
No. of bitstreams: 23
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StringQuartetEEP_I_Mechanical.zip: 351377905 bytes, checksum: 525c09a65df8cc7b9fc677b6cd609093 (MD5)
StringQuartetEEP_I_Normalj.zip: 337050645 bytes, checksum: 0dec0d5bba4bdddb0e1dce0d70fe5fca (MD5)
StringQuartetEEP_P1_Ensemble.zip: 55629077 bytes, checksum: 0b0267f7bd56efb00acba6d7cc45c981 (MD5)
StringQuartetEEP_P1_SoloCello.zip: 22711824 bytes, checksum: 2196f5948f8b636ac48bca71eb463b32 (MD5)
StringQuartetEEP_P1_SoloViola.zip: 14093106 bytes, checksum: 8715db694b10fc2f99bf964c70a28957 (MD5)
StringQuartetEEP_P1_SoloVl1.zip: 25332529 bytes, checksum: 8ced98b6d1821516672cca881f8c5f89 (MD5)
StringQuartetEEP_P1_SoloVl2.zip: 9374517 bytes, checksum: 60fd4630dbd630464a0055a6d686650f (MD5)
StringQuartetEEP_P2_Ensemble.zip: 56709331 bytes, checksum: 7235494b38c6713c3cc99a1757bdadc2 (MD5)
StringQuartetEEP_P2_SoloCello.zip: 21890196 bytes, checksum: de28f8e4593b4d75abb26bc4b01242a9 (MD5)
StringQuartetEEP_P2_SoloViola.zip: 15395954 bytes, checksum: 8f54156a0868796b7dc34cac17d65dc4 (MD5)
StringQuartetEEP_P2_SoloVl1.zip: 27766571 bytes, checksum: 2858a906a8ab635daac9be8e04024b88 (MD5)
StringQuartetEEP_P2_SoloVl2.zip: 9983274 bytes, checksum: 8d6ea70903bf4149f50a073e53941c81 (MD5)
StringQuartetEEP_P3_Ensemble.zip: 108557877 bytes, checksum: 883d8518114647c03d255ae435a20701 (MD5)
StringQuartetEEP_P3_SoloCello.zip: 44213112 bytes, checksum: 66c2241ff04a1fd53e8ed051442b59f8 (MD5)
StringQuartetEEP_P3_SoloViola.zip: 43890546 bytes, checksum: 652581ae66dd71798da002ef580e36f6 (MD5)
StringQuartetEEP_P3_SoloVl1.zip: 47611391 bytes, checksum: db9e16bfc1d70bff3939d23888a67af0 (MD5)
StringQuartetEEP_P3_SoloVl2.zip: 21708873 bytes, checksum: fee34b9be39b65c38af1403f3e1c93fd (MD5)
StringQuartetEEP_P4_Ensemble.zip: 48399974 bytes, checksum: 720d5446c959fbe2936dadf4be841278 (MD5)
StringQuartetEEP_P4_SoloCello.zip: 15983256 bytes, checksum: 89bfd258eb669aac24e217bb0944b468 (MD5)
StringQuartetEEP_P4_SoloViola.zip: 16603859 bytes, checksum: f0c41414dd7cfb0f88f49444496768dc (MD5)
StringQuartetEEP_P4_SoloVl1.zip: 15283011 bytes, checksum: 26dc927243080e88e7850ebff87df7f4 (MD5)
StringQuartetEEP_P4_SoloVl2.zip: 7046382 bytes, checksum: 226a9417b632ddd26c4933c93b2ddcca (MD5)
Made available in DSpace on 2017-03-21T12:19:12Z (GMT). No. of bitstreams: 23
StringQuartetEEP_I_Exaggerated.zip: 233020198 bytes, checksum: 058a8611ce6402f078aba033fad6b3ac (MD5)
StringQuartetEEP_I_Mechanical.zip: 351377905 bytes, checksum: 525c09a65df8cc7b9fc677b6cd609093 (MD5)
StringQuartetEEP_I_Normalj.zip: 337050645 bytes, checksum: 0dec0d5bba4bdddb0e1dce0d70fe5fca (MD5)
StringQuartetEEP_P1_Ensemble.zip: 55629077 bytes, checksum: 0b0267f7bd56efb00acba6d7cc45c981 (MD5)
StringQuartetEEP_P1_SoloCello.zip: 22711824 bytes, checksum: 2196f5948f8b636ac48bca71eb463b32 (MD5)
StringQuartetEEP_P1_SoloViola.zip: 14093106 bytes, checksum: 8715db694b10fc2f99bf964c70a28957 (MD5)
StringQuartetEEP_P1_SoloVl1.zip: 25332529 bytes, checksum: 8ced98b6d1821516672cca881f8c5f89 (MD5)
StringQuartetEEP_P1_SoloVl2.zip: 9374517 bytes, checksum: 60fd4630dbd630464a0055a6d686650f (MD5)
StringQuartetEEP_P2_Ensemble.zip: 56709331 bytes, checksum: 7235494b38c6713c3cc99a1757bdadc2 (MD5)
StringQuartetEEP_P2_SoloCello.zip: 21890196 bytes, checksum: de28f8e4593b4d75abb26bc4b01242a9 (MD5)
StringQuartetEEP_P2_SoloViola.zip: 15395954 bytes, checksum: 8f54156a0868796b7dc34cac17d65dc4 (MD5)
StringQuartetEEP_P2_SoloVl1.zip: 27766571 bytes, checksum: 2858a906a8ab635daac9be8e04024b88 (MD5)
StringQuartetEEP_P2_SoloVl2.zip: 9983274 bytes, checksum: 8d6ea70903bf4149f50a073e53941c81 (MD5)
StringQuartetEEP_P3_Ensemble.zip: 108557877 bytes, checksum: 883d8518114647c03d255ae435a20701 (MD5)
StringQuartetEEP_P3_SoloCello.zip: 44213112 bytes, checksum: 66c2241ff04a1fd53e8ed051442b59f8 (MD5)
StringQuartetEEP_P3_SoloViola.zip: 43890546 bytes, checksum: 652581ae66dd71798da002ef580e36f6 (MD5)
StringQuartetEEP_P3_SoloVl1.zip: 47611391 bytes, checksum: db9e16bfc1d70bff3939d23888a67af0 (MD5)
StringQuartetEEP_P3_SoloVl2.zip: 21708873 bytes, checksum: fee34b9be39b65c38af1403f3e1c93fd (MD5)
StringQuartetEEP_P4_Ensemble.zip: 48399974 bytes, checksum: 720d5446c959fbe2936dadf4be841278 (MD5)
StringQuartetEEP_P4_SoloCello.zip: 15983256 bytes, checksum: 89bfd258eb669aac24e217bb0944b468 (MD5)
StringQuartetEEP_P4_SoloViola.zip: 16603859 bytes, checksum: f0c41414dd7cfb0f88f49444496768dc (MD5)
StringQuartetEEP_P4_SoloVl1.zip: 15283011 bytes, checksum: 26dc927243080e88e7850ebff87df7f4 (MD5)
StringQuartetEEP_P4_SoloVl2.zip: 7046382 bytes, checksum: 226a9417b632ddd26c4933c93b2ddcca (MD5)
Previous issue date: 2014
This work was partially supported by the EU FP7 FET-Open SIEMPRE Project (FP7-ICT- 2009-C-250026), by the Spanish TIN project DRIMS (TIN2009-14274-C02-01), and by the Catalan Research Funding Agency AGAUR.
eng
Datasets available from RepoVizz
Més informació: Music Technology Group (MTG)
Publicació relacionada: Marchini M, Ramirez R, Papiotis P, Maestre E. The sense of ensemble: a machine learning approach to expressive performance modelling in string quartets. J New Music Res. 2014;43(3):303-17. DOI: 10.1080/09298215.2014.922999
http://dx.doi.org/10.1080/09298215.2014.922999
http://repovizz.upf.edu/
http://mtg.upf.edu/download/datasets/eep-dataset
info:eu-repo/grantAgreement/EC/FP7/250026
info:eu-repo/grantAgreement/ES/3PN/TIN2009-14274-C02-01
Copyright © 2014 Music Technology Group, Universitat Pompeu Fabra. All Rights Reserved.
The MTG-EEP dataset is offered free of charge for internal non-commercial use only. You may not redistribute, publically communicate or modify it. Please see the license terms in the README file within the dataset for applicable conditions.
info:eu-repo/semantics/openAccess
Ensemble Expressive Performance Dataset (EEP)
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/308152018-02-19T10:51:38Zcom_10230_5963col_10230_24646
Santamaría, Guillermo
Gómez, Vicenç
2017-04-11T10:49:47Z
2017-04-11T10:49:47Z
2017
Santamaría G, Gómez V. Convex inference for community discovery in signed networks (European Parliament Voting Dataset) [dataset]. Repositori Digital de la UPF: Barcelona;2017 [cited 2017 Apr 11]. Available from: http://hdl.handle.net/10230/30815
http://hdl.handle.net/10230/30815
http://dx.doi.org/10.5281/zenodo.345950
This repository contains the necessary tools to reproduce the experiments of the paper:
G. Santatmaría, V. Gómez (2015)
Convex inference for community discovery in signed networks.
NIPS 2015 Workshop: Networks in the Social and Information Sciences
------The method first maps the MAP problem on the Potts model as a hinge-loss minimization problem (see the paper for details). To run the code you need to install psl (included here) and if you want to additionally compare with other inference methods, such as max prod belief propagation or junction tree, you need to install the libDAI library (also included here)
--------The directory europeanCongressData/ (~500 Mb) contains the votings of the EU parlament, including 300 votings events from the actual term, from May 2014 to June 2015, obtained from http://www.votewatch.eu/
* data/ : json files with the european votes
* network.net : signed network built from the votes
* political_parties.txt : "ground truth" party
* community_results/ : results for different number of communities and initial vertices
* dataComputations.py : used to build the signed network
* dataProcessing.py : used to build the signed network
We would appreciate if you cite the paper after using the data or the code.
--------DEPENDENCIES--------
The code has been tested in Linux Mint 18.1 Serena and Ubuntu 14.04
- For PSL library, you need to have
java 1.8
you may need to export JAVAHOME='/usr/lib/jvm/YOURJAVA1.8FOLDER'
maven 3.x
- For libDAI you will need:
make doxygen graphviz libboost-dev libboost-graph-dev libboost-program-options-dev libboost-test-dev libgmp-dev cimg-dev libgmp-dev
--------CODE TO RUN THE FOLLOWING EXPERIMENTS:--------
Compare the performance in terms of structural balance of max prod bp and our method against an exact inference method (junction tree), with different number of communities
--------INSTALL--------
To install the experiments you have to follow the next steps:
1 Build the libdai library by doing: make -B on the folder (libdai)
2 Generate the class path of the groovy project:
mvn clean install
mvn dependency:build-classpath-Dmdep.outputFile=classpath.out
on the psl root folder (You need to have java 1.8 and maven 3.x installed)
3 Grant exec permissions to the run.sh script
--------Options--------
The main python file to run the experiments is
evaluatebalanceon_sn.py.
It accepts the following parameters:
1 (Int) Nodes of the graph. In order to run the junction tree we recommend to set this paremeter to 150 or less
2 (Int) The number of underlying communities
3 (Float) The maximum amount of unbalance for the experiments. We recommend 0.45
4 (Bool) Whether to use an heuristic to find the initial node for each community or to use directly random nodes from the ground truth communities. This heuristic looks alternatively for the nodes with highest negative degree and highest positive degree. For the case when the number of communities is equal to 2 (Ising Model), the heuristic is used by default.
An example of execution would be:
python evaluate_balance_on_sn.py 120 3 0.45 True True
The results of the experiments are save in the folder results/
Scripts
The main script of the hinge-loss method can be found in the folder psl/psl-example/src/main/java/edu/umd/cs/example/PottsCommunities.groovy
--------For further questions, please contact vicen.gomez@upf.edu
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2017-04-11T10:49:47Z
No. of bitstreams: 1
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potss_communities.zip: 69185128 bytes, checksum: 8904651234876cf57574a6e2d77eb16e (MD5)
Previous issue date: 2017
eng
Publicació relacionada: Santamaría G, Gómez V. Convex inference for community discovery in signed networks. Paper presented at: NIPS 2015 Workshop: Networks in the Social and Information Sciences; 2015 December 12; Montreal, Canada. http://hdl.handle.net/10230/32243
http://hdl.handle.net/10230/32243
info:eu-repo/grantAgreement/EC/FP7/600387
Aquest dataset està subjecte a una llicència Creative Commons
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Convex inference for community discovery in signed networks (European Parliament Voting Dataset)
info:eu-repo/semantics/other
Dataset
Signed networks
Community discovering
Social networks
Potts model
European Parliament Voting
oai:repositori.upf.edu:10230/337742018-01-29T11:26:54Zcom_10230_5963col_10230_24646
Rankothge, Windhya
Le, Franck
Russo, Alessandra
Lobo, Jorge
2018-01-29T10:56:11Z
2018-01-29T10:56:11Z
2017
http://hdl.handle.net/10230/33774
##Project Structure:
1. GeneratePolicies.
2. DistributeTrafficOverPolicies.
3. PoliciesToChange.
4. TopologyCreator.
5. ExampleDataSet.
##Guidelines to use the data and programs in the repository.
There are two ways that this repository can be useful for anyone that needs data about VNFs and their traffic on the cloud.
1.Directly use the already generated data set.
2.Generate your own data set using the given programs.
##How to use the already generated data set: ExampleDataSet.
We have generated data for:
1.Possible policy requests with initial traffic passing through them defined.
2.Scaling requirements for each 15 minutes for 2 days.
3.Topology data (nodes, links, paths) for K-Fat Tree, BCube and VL2 architectures with 64 servers.
You can use these data directly as inputs for your experiments.
##How to use the programs and generate the required data sets.
If you want to generate your own data sets according to your requirements, you can use the given programs.
1) First step is to generate the policy requests data set using the policy requests generation program: GeneratePolicies.
- Inputs to the program: number of large scaled enterprise networks.
- Output of the program: a set of policies for each enterprise with 100 NFs.
2) After we have created the policy requets data set, the seconds step is to create the traffic data set for the policies using the initial traffic distribution program: DistributeTrafficOverPolicies.
- Inputs to the program: the set of policies, initial traffic load.
- Output of the program: distribution of the traffic load over policies.
3) The third step is to create the scaling requirements data set to reflect the traffic changes over the time using the scaling requirements over the time program: PoliciesToChange.
4) The last step is to generate the required topology data for different network architectures (K-Fat tree, BCube, VL2) using the topology generation program: TopologyCreator.
- Inputs to the program: network architecture and number of servers.
- Output of the program: the topology: nodes, links and paths.
Network Function Virtualization (NFV) proposes to move packet processing from dedicated hardware middle-boxes to software running on commodity servers: virtualized Network Function (NFs) (i.e, Firewall, Proxy, Intrusion Detection System etc.). We have been developing an experimental platform called Network Function Center (NFC) to study issues related to NFV and NFs, assuming that the NFC will deliver virtualized NFs as a service to clients on a subscription basis. Our studies specially focus on dynamic resource allocation for NFs and we have proposed two new resource allocation algorithms based on Genetic Programming (GP) [1] and currently working on another algorithm based on Iterative Local Search. For a more realistic evaluation of these algorithms, testing data is a fundamental component, but unfortunately, public traffic data specifically referring to virtualized NFs chains is not readily available. Therefore, we developed a model to generate the specific data we needed, based on the available general traffic data [2].
This repository contains all the details about how we modelled general data into the specific data we wanted, with along the software we used and the assumptions we made during the data modelling process. Using this data and programs, the evaluation results presented in our publications can be easily reproduced.
[1] W. Rankothge, J. Ma, F. Le, A. Russo, and J. Lobo, [“Towards making network function virtualization a cloud computing service,”] (http://repositori.upf.edu/handle/10230/26035) in IM 2015.
[2] W. Rankothge, F. Le, A. Russo, and J. Lobo, [“Experimental results on the use of genetic algorithms for scaling virtualized network functions,”] (http://repositori.upf.edu/handle/10230/26036) in IEEE SDN/NFV 2015.
Submitted by Dades de Recerca (repositori-becari3@upf.edu) on 2018-01-29T10:56:11Z
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Rankothge_dataNFVSDNexperiments.zip: 722434 bytes, checksum: aa4815f8b9f3277d9f3bb7c0b85fe87a (MD5)
Previous issue date: 2017
eng
Universitat Pompeu Fabra
Publicació relacionada: Rankothge, Windhya. Towards virtualized network functions as a service. 2017
http://hdl.handle.net/10803/402892
Publicació relacionada: Rankothge W, Ma J, Le F, Russo A, Lobo J. Experimental results on the use of genetic algorithms for scaling virtualized network functions. In: 2015 IEEE Conference on Virtualization and Software Defined Network (NFV-SDN); 2015 Nov 18-21; San Francisco, CA. IEEE; 2015. p. 47-53. DOI 10.1109/NFV-SDN.2015.7387405. http://hdl.handle.net/10230/26036
Publicació relacionada: Rankothge W, Ma J, Le F, Russo A, Lobo J. Towards making network function virtualization a cloud computing service. In: Proceedings of the/n2015 IFIP/IEEE International Symposium on Integrated Network Management (IM); 2015 May 11-15; Ottawa, Canada. IEEE; 2015. p. 89-97. DOI 10.1109/INM.2015.7140280. http://hdl.handle.net/10230/26035
http://hdl.handle.net/10803/402892
http://hdl.handle.net/10230/26036
http://hdl.handle.net/10230/26035
The dataset and all the software programs are distributed under the terms of the GNU General Public License v3.
http://www.gnu.org/licenses/gpl-3.0-standalone.html
info:eu-repo/semantics/openAccess
Data for NFVSDN experiments
info:eu-repo/semantics/other
Dataset
Network function virtualization
Software defined networks
Cloud resource management
Virtualización de funciones de redes
Redes definidas por software
Administración de recursos de la nube
oai:repositori.upf.edu:10230/328582018-02-19T10:52:17Zcom_10230_5963col_10230_24646
Wang, Xingce
Liu, Yue
Wu, Zhongke
Mou, Xiao
Zhou, Mingquan
González Ballester, Miguel Ángel, 1973-
2017-10-05T09:44:07Z
2017-10-05T09:44:07Z
2017-06-15
http://hdl.handle.net/10230/32858
http://dx.doi.org/10.5281/zenodo.809931
##Compile environment
Windows 7(64-bit)
Intel(R) Core(TM) i7-4790 CPU @ 3.6GHz
RAM: 8.00GB
Microsoft Visual Studio 2012
Python 2.7
Anaconda 4.1.0 (64-bit)
XGBoost Library 0.4 (https://github.com/dmlc/xgboost/tree/master/windows)
Scikit-Learn Library 0.18.1
hmmlearn 0.2.0
NURBS open-source library
## Running the code
This file contains a summary of what you will find in each of the files that make up our experiments..
Step0: PreprocessingData
Our proposed approach has been evaluated on the public dataset distributed by the MIDAS Data Server at Kitware Inc.. It contains 50 MRA images of the cerebral vasculature from healthy volunteers together with theirs segmentations and centerlines. (Bogunović et al. "Anatomical Labeling of the Circle of Willis Using Maximum A Posteriori Probability Estimation." IEEE Transactions on Medical Imaging 32(9) (2013):1587)
We first prune the centerline model to a region around the CoW. “FeatureGenerating/data/skeleton”.
Step1: FeatureGenerating(C/C++):
To generate a feature matrix “FeatureGenerating/feature” from the skeleton data “FeatureGenerating/data/skeleton” that has been marked with Ground Truth“FeatureGenerating/data/cood.txt”. We employed the NURBS curve with features calculate available in NURBS open-source library. To compile the code, you also need to include the library.
Step2: Pre_ML (C/C++):
To Separate feature matrix “FeatureGenerating/feature” into the training set “data/ML/XXX/train.txt” and corresponding test set “data/ML/XXX/test.txt”.
Step3: XGBoost(Python):
To train model based on the training set in “data/ML”, and predict the results ”data/res_XGBoost” of corresponding test set. To compile the code, you also need to include the XGBoost library.
Step4: Chain(C/C++):
To “sort” the bifurcation and construct observation sequences “data/obs_list” and status sequences “data/GT_list” based on the results of XGBoost.
Step5: Pre_HMM(C/C++):
To generates 50 sets of observation matrices “data/obs” and transfer matrices “data/trans” based on observation sequences and state sequences. Row 1 in “seg/XXX” is the sequence of state, and Row 2 in “data/seg/XXX” is its corresponding sequence of observations.
Step6: HMM(Python):
Hidden Markov Process.
Input:”data/seg/XXX”, “data/obs”, “data/trans”
In the file “data/res_topo”, Row 1 is the results, and Row 2 is its corresponding Ground Truth. To compile the code, you also need to include the hmmlearn library.
Step7: Result analysis:
Metrics. In the file”data/matrix_XGBoost”and ”data/matrix_topo”, the first part is TP, FN, FP, TN value, the second part is A, P, R, S value, the last part is the confusion matrix.
The project contains the implementation of the method described in:
Wang et al., "Automatic labeling of vascular structures with topological constraints via HMM", MICCAI 2017.
We propose a novel graph labeling approach to anatomically label vascular structures of interest. Our algorithm can handle different topologies, like circle, chain and tree. By using coordinate independent geometrical features, it does not require prior global alignment.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2017-10-05T09:44:07Z
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VesselLabelingViaHMM_MICCAI2017.zip: 7851796 bytes, checksum: cd8f971bcd7e1e700e3a0588d7d43aed (MD5)
Previous issue date: 2017-06-15
This research was partially supported by the Chinese High-Technical Research
Development Foundation (863) Program (No.2015AA020506), Beijing Natural
Science Foundation of China(No.4172033), the Spanish Ministry of Economy and
Competitiveness, through the Maria de Maeztu Programme for Centres/Units
of Excellence in R&D (MDM-2015-0502), and the Spanish Ministry of Economy
and Competitiveness (DEFENSE project, TIN2013-47913-C3-1-R).
eng
Universitat Pompeu Fabra
Publicació relacionada: Wang X, Liu Y, Wu Z, Mou X, Zhou M, González Ballester MA, Zhang C. Automatic labeling of vascular structures with topological constraints via HMM. Paper presented at: 20th International Conference on Medical Image Computing and Computer Assisted Intervention 2017 (MICCAI 2017); 2017 Sept 10-14; Quebec, Canada. [8 p.]. http://dx.doi.org/10.5281/zenodo.809931 http://hdl.handle.net/10230/32858
http://hdl.handle.net/10230/32744
info:eu-repo/grantAgreement/ES/1PE/TIN2013-47913-C3-1-R
Aquest material està subjecte a una llicència de Creative Commons (Attribution-NonCommercial 4.0)
https://creativecommons.org/licenses/by-nc/4.0/
info:eu-repo/semantics/openAccess
Automatic labeling of vascular structures with topological constraints via HMM [Research data]
info:eu-repo/semantics/other
Dataset
info:eu-repo/semantics/publishedVersion
oai:repositori.upf.edu:10230/337292018-02-19T10:52:36Zcom_10230_5963col_10230_24646
Sentürk, Sertan
2018-01-23T10:26:07Z
2018-01-23T10:26:07Z
2016
Sentürk, Sertan. Turkish-Ottoman Makam (M)usic Analysis TOolbox (tomato). Repositori digital de la UPF: Barcelona; 2016. Disponible a: http://hdl.handle.net/10230/33729
http://hdl.handle.net/10230/33729
Tomato is a comprehensive and easy-to-use toolbox in Python for the analysis of audio recordings and music scores of Turkish-Ottoman makam music. The toolbox includes the state of art methodologies applied on this music tradition. The analysis tasks include:
* Audio Analysis: audio metadata crawling, predominant melody extraction, tonic and transposition identification, makam recognition, histogram analysis, tuning analysis, melodic progression analysis
* Symbolic Analysis: score metadata extraction, score section extraction, score phrase segmentation, semiotic section and phrase analysis
* Joint Analysis: score-informed tonic identification and tempo estimation, section linking, note-level audio-score alignment, predominant melody octave correction, note models, (usul tracking is coming soon)
The aim of the toolbox is to facilitate the analysis of large-scale audio recording and music score collections of Turkish-Ottoman makam music, using the state of the art methodologies specifically designed for the culture-specific characteristics of this tradition. The analysis results can then be further used for several tasks such as automatic content description, music discovery/recommendation and musicological analysis.
Research data from the thesis "Computational Analysis of Audio Recordings and Music Scores for the Description and Discovery of Ottoman-Turkish Makam Music".
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2018-01-23T10:26:07Z
No. of bitstreams: 1
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tomato-master.zip: 7313316 bytes, checksum: a96c5a6ea178dd22bd461cdc4861ba37 (MD5)
Previous issue date: 2016
eng
Publicació relacionada: Sentürk S. Computational Analysis of Audio Recordings and Music Scores for the Description and Discovery of Ottoman-Turkish Makam Music. Barcelona: Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/402102
http://hdl.handle.net/10803/402102
https://github.com/sertansenturk/tomato
This resource is licensed under a Affero GPL version 3 (https://www.gnu.org/licenses/agpl-3.0.en.html)
info:eu-repo/semantics/openAccess
Turkish-Ottoman Makam (M)usic Analysis TOolbox (tomato)
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/338022018-02-05T09:06:40Zcom_10230_5963col_10230_24646
Barbieri, Francesco
Ronzano, Francesco
Saggion, Horacio
2018-02-05T08:53:18Z
2018-02-05T08:53:18Z
2016
Barbieri F, Ronzano F, Saggion H. EmoTwi50 [research data]. Repositori Digital de la UPF: Barcelona; 2016. Disponible a: http://hdl.handle.net/10230/33802
http://hdl.handle.net/10230/33802
The dataset is a TSV (tab-separated) with five columns: the first two columns represent the codes of the pair of emojis evaluated, the third column their gold standard similarity, the fourth column their gold standard relatedness and the fifth column the average of the previous two values. Each row of the file represents the gold standard evaluation results of a pair of emojis. Remember that in order to retrieve the vectorial embedding corresponding to an emoji in our models, you need to add the token "eoji" before the emoji code.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2018-02-05T08:53:18Z
No. of bitstreams: 1
EmoTwi50_gold.tsv: 1409 bytes, checksum: 19c9c7ecfcf3b868f30ac75c029b88e3 (MD5)
Made available in DSpace on 2018-02-05T08:53:18Z (GMT). No. of bitstreams: 1
EmoTwi50_gold.tsv: 1409 bytes, checksum: 19c9c7ecfcf3b868f30ac75c029b88e3 (MD5)
Previous issue date: 2016
eng
Universitat Pompeu Fabra
Publicació relacionada: Barbieri F, Ronzano F, Saggion H. What does this emoji mean? A vector space skip-gram model for twitter emojis. In: Calzolari N, Choukri K, Declerck T, et al, editors. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016); 2016 May 23-28; Portorož, Slovenia. Paris: European Language Resources Association (ELRA); 2016. p.3967-72. http://hdl.handle.net/10230/33776
http://hdl.handle.net/10230/33776
All these materials are frely available under Creative Commons CC BY 3.0
https://creativecommons.org/licenses/by/3.0/
info:eu-repo/semantics/openAccess
EmoTwi50 [research data]
info:eu-repo/semantics/other
Dataset
info:eu-repo/semantics/publishedVersion
oai:repositori.upf.edu:10230/339812019-04-02T12:40:48Zcom_10230_5963col_10230_24646
Öktem, Alp
Farrús, Mireia
Lai, Catherine
2018-02-23T09:19:16Z
2018-02-23T09:19:16Z
2018-02-23
Öktem A, Farrús M, Lai C. Prosodically annotated TED talks. Repositori Digital de la UPF: Barcelona; 2018. Disponible a: http://hdl.handle.net/10230/33981
http://hdl.handle.net/10230/33981
"Audio files of the recordings are provided in the partitioned archives as WAV format. ""talk_proscripts"" archive contains Proscript format annotations of complete talks. ""punkProse_dataset"" archive contains sampled dataset partitioning used in prosodic punctuation modelling experiments (See http://github.com/alpoktem/punkProse). README.txt file contains information on the dataset and authors. Indexing of the files and their corresponding talks are listed in TED_talk_ids.txt.
Proscript format files contain the sequence of uttered words in a recording, their approximate timings and corresponding acoustic measurements (pitch, intensity, speech rate). For more information on Proscript format see http://github.com/alpoktem/proscript."
TED talks are a set of conference talks that have been held worldwide in more than 100 languages. They include a large variety of topics, from technology and design to science, culture and academia. This corpus consists of speech recordings and Proscript format annotations of 1046 talks by 877 English speakers, uttering a total amount of 155174 sentences.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2018-02-23T09:19:16Z
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eng
Universitat Pompeu Fabra
Publicació relacionada: Öktem A, Farrús M, Wanner L. Attentional parallel RNNs for generating punctuation in transcribed speech. In: Camelin N, Estève Y, Martín-Vide C. Statistical Language and Speech Processing. 5th International Conference SLSP 2017; 2017 Oct 23-25; Le Mans, France. Cham: Springer, 2017. p. 131-42. (LNCS; no. 10583 ). DOI: 10.1007/978-3-319-68456-7_11 http://hdl.handle.net/10230/33936
Sotware relacionat: http://hdl.handle.net/10230/33982
http://hdl.handle.net/10230/33936
http://hdl.handle.net/10230/33982
info:eu-repo/grantAgreement/EC/H2020/645012
Attribution 4.0 International (CC BY 4.0)
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Prosodically annotated TED talks
info:eu-repo/semantics/other
Dataset
Speech transcription
Recurrent neural networks
Prosody
Punctuation generation
Automatic speech recognition
Speech dataset
Conference talks
oai:repositori.upf.edu:10230/339822018-02-23T12:04:43Zcom_10230_5963col_10230_24646
Öktem, Alp
2018-02-23T09:40:52Z
2018-02-23T09:40:52Z
2018-02
Öktem A. PunkProse [software]. Repositori Digital de la UPF: Barcelona; 2018. Disponible a: http://hdl.handle.net/10230/33982
http://hdl.handle.net/10230/33982
This software is stored and maintained in the following github repository: https://github.com/alpoktem/punkProse
Instructions to use is explained there in detail.
Punctuation marks support understandability and readability in written language. In spoken language, punctuation of the transcribed speech is influenced by two phenomena: (1) syntax and (2) prosody. We present a software architecture that makes it possible to train punctuation restoration models from any combination of lexical, morphosyntactic, prosodic and acoustic features. Architecture is language independent and feeds on word-segmented data. A dataset compiled from English TED talks is given in http://hdl.handle.net/10230/33981
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2018-02-23T09:40:52Z
No. of bitstreams: 1
punkProse-master.zip: 1242692 bytes, checksum: 98cf08fae8eb195732e7660145ae477d (MD5)
Made available in DSpace on 2018-02-23T09:40:52Z (GMT). No. of bitstreams: 1
punkProse-master.zip: 1242692 bytes, checksum: 98cf08fae8eb195732e7660145ae477d (MD5)
Previous issue date: 2018-02
Universitat Pompeu Fabra
Publicació relacionada: Öktem A, Farrús M, Wanner L. Attentional parallel RNNs for generating punctuation in transcribed speech. In: Camelin N, Estève Y, Martín-Vide C. Statistical Language and Speech Processing. 5th International Conference SLSP 2017; 2017 Oct 23-25; Le Mans, France. Cham: Springer, 2017. p. 131-42. (LNCS; no. 10583 ). DOI: 10.1007/978-3-319-68456-7_11 http://hdl.handle.net/10230/33936
https://github.com/alpoktem/punkProse
Dades relacionades: http://hdl.handle.net/10230/33981
http://hdl.handle.net/10230/33936
http://hdl.handle.net/10230/33981
info:eu-repo/grantAgreement/EC/H2020/645012
The MIT License (MIT)
Copyright © 2016 Ottokar Tilk
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
info:eu-repo/semantics/openAccess
PunkProse [software]
info:eu-repo/semantics/other
Software
info:eu-repo/semantics/updatedVersion
Speech transcription
Recurrent neural networks
Prosody
Punctuation generation
Automatic speech recognition
oai:repositori.upf.edu:10230/341912018-03-17T02:31:29Zcom_10230_5963col_10230_24646
Oramas, Sergio
2018-03-16T12:09:29Z
2018-03-16T12:09:29Z
2018-03-16
Oramas S. FLABASE: A Flamenco Knowledge Base [dataset]. Repositori Digital de la UPF: Barcelona; 2018. Disponible a: http://hdl.handle.net/10230/34191
http://hdl.handle.net/10230/34191
Data was compiled and curated from different sources: Wikipedia, DBpedia, Andalucia.org, elartedevivirelflamenco.com, MusicBrainz, flun.cica.es/index.php/grabaciones/base-datos-grabaciones and juntadeandalucia.es/institutodeestadisticaycartografia/sima
FlaBase (Flamenco Knowledge Base) is the acronym of a new knowledge base of flamenco music. Its ultimate aim is to gather all available online editorial, biographical and musicological information related to flamenco music. A first version is just being released. Its content is the result of the curation and extraction processes. FlaBase is stored in JSON format, and it is freely available for download. This first release of FlaBase contains information about 1,102 artists, 74 palos (flamenco genres), 2,860 albums, 13,311 tracks, and 771 Andalusian locations.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2018-03-16T12:09:29Z
No. of bitstreams: 2
flabase-json.zip: 6216066 bytes, checksum: 15408644c6b89f85ece4011f6c3de010 (MD5)
readme.txt: 3651 bytes, checksum: 34754e64c5a4303501ada75444675d9e (MD5)
Made available in DSpace on 2018-03-16T12:09:29Z (GMT). No. of bitstreams: 2
flabase-json.zip: 6216066 bytes, checksum: 15408644c6b89f85ece4011f6c3de010 (MD5)
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eng
spa
Publicació relacionada: Oramas S, Gómez F, Gómez E, Mora J. FlaBase: Towards the creation of a flamenco music knowledge base. In: Müller M, Wiering F, editors. ISMIR 2015. 16th International Society for Music Information Retrieval Conference; 2015 Oct 26-30; Málaga, Spain. Canada: ISMIR; 2015. p. 378-84. http://hdl.handle.net/10230/33415
http://hdl.handle.net/10230/33415
info:eu-repo/grantAgreement/ES/3PN/TIN2012-36650
Dataset compiled and authored by Sergio Oramas. Copyright ©2015 Music Technology Group, Universitat Pompeu Fabra. All Rights Reserved.
info:eu-repo/semantics/openAccess
FLABASE: A Flamenco Knowledge Base
info:eu-repo/semantics/other
Dataset
info:eu-repo/semantics/publishedVersion
Flamenco
Information Extraction
Knowledge Base
Semantic Web
Music
oai:repositori.upf.edu:10230/343252018-04-10T08:12:29Zcom_10230_5963col_10230_24646
Oramas, Sergio
2018-04-09T10:30:46Z
2018-04-09T10:30:46Z
2016
Oramas S. MARD: Multimodal Album Reviews Dataset. Repositori Digital de la UPF: Barcelona; 2018. Disponible a: http://hdl.handle.net/10230/34325
http://hdl.handle.net/10230/34325
MARD contains texts and accompanying metadata originally obtained from a much larger dataset of Amazon customer reviews, which have been enriched with music metadata from MusicBrainz, and audio descriptors from AcousticBrainz. MARD amounts to a total of 65,566 albums and 263,525 customer reviews.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2018-04-09T10:30:45Z
No. of bitstreams: 1
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mard.zip: 334518581 bytes, checksum: bb6bbbc913db86036b85363f2367682e (MD5)
Previous issue date: 2016
This work was partially funded by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
eng
Publicació relacionada: Oramas S, Espinosa-Anke L, Lawlor A, Serra X, Saggion H. Exploring customer reviews for music genre classification and evolutionary studies. In: Devaney J, Mandel MI, Turnbull D, Tzanetakis G, editors. ISMIR 2016. Proceedings of the 17th International Society for Music Information Retrieval Conference; 2016 Aug 7-11; New York City (NY). [Canada]: ISMIR; 2016. p. 150-6. http://hdl.handle.net/10230/33063
http://hdl.handle.net/10230/33063
Dataset compiled by Sergio Oramas based on a previous dataset by Julien McAuley. Copyright ©2016 Music Technology Group, Universitat Pompeu Fabra. All Rights Reserved. The MARD dataset is offered under MIT license.
info:eu-repo/semantics/openAccess
MARD: Multimodal Album Reviews Dataset
info:eu-repo/semantics/other
Dataset
Sentiment analysis
Natural language processing
Music
Album reviews
Musicology
oai:repositori.upf.edu:10230/352992018-07-26T10:26:33Zcom_10230_5963col_10230_24646
Soler Company, Juan
2018-07-26T10:23:13Z
2018-07-26T10:23:13Z
2017
http://hdl.handle.net/10230/35299
El zip conté tots els recursos que s'han generat durant el desenvolupament de la tesi. Per una banda, hi ha el codi, amb el qual es poden extreure el conjunt de features tal i com es descriu a la tesi, per altre banda, hi ha també tots els datasets que s'han creat i que s'utilitzen per a tots els experiments. Utilitzant el codi, les eines externes corresponents i els datasets, es poden emular tots els experiments descrits.
The zip file contains every resource that has been generated during the development of the thesis. One of the folders contains the code that is used to extract the described feature set, the other one contains every dataset that has been compiled and used in every experiment. Using the code, the external tools mentioned in the experiments and the corpora, it is possible to repeat every experiment described in the thesis.
Submitted by Dades de Recerca (repositori-becari3@upf.edu) on 2018-07-26T10:23:12Z
No. of bitstreams: 2
Resources.zip: 86443746 bytes, checksum: 6f8fc8ded2c248c3826e13ed17fd4b8a (MD5)
license_rdf: 1089 bytes, checksum: 0a703d871bf062c5fdc7850b1496693b (MD5)
Made available in DSpace on 2018-07-26T10:23:13Z (GMT). No. of bitstreams: 2
Resources.zip: 86443746 bytes, checksum: 6f8fc8ded2c248c3826e13ed17fd4b8a (MD5)
license_rdf: 1089 bytes, checksum: 0a703d871bf062c5fdc7850b1496693b (MD5)
Previous issue date: 2017
eng
Publicació relacionada: Soler Company, Juan. Feature engineering for author profiling and identification: on the relevance of syntax and discourse. 2017. http://hdl.handle.net/10803/404984
http://hdl.handle.net/10803/404984
CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/
info:eu-repo/semantics/openAccess
Tractament del llenguatge natural (Informàtica)
Author profiling resources
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/353172018-07-28T01:31:07Zcom_10230_5963col_10230_24646
Ó Nuanáin, Cárthach
2018-07-27T10:33:01Z
2018-07-27T10:33:01Z
2018-07
Ó Nuanáin C. Timbre Classification experiments [dataset]. Repositori Digital de la UPF: Barcelona; 2018. Disponible a: http://hdl.handle.net/10230/35317
http://hdl.handle.net/10230/35317
This repository contains datasets and scripts for timbre classification experiments conducted as part the Ph.D. thesis. Two datasets were used. The first one concentrates on drum/percussion sounds while the other generalises to orchestral sounds. See the relevant iPython notebooks to re-run experiments.
The orchestral sample is quite large, there is a script that pulls N number samples randomly in the folder, for performing smaller analyses.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2018-07-27T10:33:01Z
No. of bitstreams: 2
timbreClassification-master.zip: 485740934 bytes, checksum: b46871c0f288e243a7035997838b4abc (MD5)
readme.txt: 1017 bytes, checksum: 535e298d70cbda7b8e5b683613463f52 (MD5)
Made available in DSpace on 2018-07-27T10:33:01Z (GMT). No. of bitstreams: 2
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Previous issue date: 2018-07
eng
Publicació relacionada: Ó Nuanáin, Cárthach. Connecting time and timbre: computational methods for generative rhythmic loops in symbolic and signal domains. 2018. http://hdl.handle.net/10803/482191
http://hdl.handle.net/10803/482191
https://github.com/carthach/timbreClassification
Creative Commons CC0 public domain license
https://creativecommons.org/publicdomain/zero/1.0/
info:eu-repo/semantics/openAccess
Timbre Classification experiments
info:eu-repo/semantics/other
Dataset
oai:repositori.upf.edu:10230/355722018-10-11T10:27:40Zcom_10230_5963col_10230_24646
Öktem, Alp
2018-10-05T10:32:10Z
2018-10-05T10:32:10Z
2018-10-05
http://hdl.handle.net/10230/35572
Each episode directory contains word-level and segment-level information of the whole episode and also parallel samples extracted under segments_eng and segments_spa subdirectories. Each sample is stored as an WAV audio file, text file and a CSV file containing word timing information and word-level paralinguistic and prosodic features.
This dataset contains short audio and text excerpts from the TV series "Heroes" (Copyright Universal Media Studios (2006-2007,2007-2008, 2008-2009)). It is compiled and used only for research purposes.
Creation of this dataset is partially financed by the UPF DTIC-Maria de Maeztu Strategic Program.
This dataset is created with automated tools. There might be errors due to the automated process.
Heroes corpus contains mapped bilingual (English and Spanish) speech segments from the TV series Heroes. It contains 7000 single speaker speech segments extracted from the original and Spanish dubbed version of 21 episodes. Audio segments are accompanied with subtitle transcriptions and word-level prosodic/paralinguistic information.
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2018-10-05T10:32:10Z
No. of bitstreams: 3
README.txt: 1568 bytes, checksum: 5590acf4cbe632f858fa54d071633857 (MD5)
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episodes_s3.rar: 791117973 bytes, checksum: 420ce734e28b55039febdde71806ab50 (MD5)
Made available in DSpace on 2018-10-05T10:32:10Z (GMT). No. of bitstreams: 3
README.txt: 1568 bytes, checksum: 5590acf4cbe632f858fa54d071633857 (MD5)
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Maria de Maeztu Programme/DTIC
eng
spa
Publicació relacionada: Öktem A, Farrús M, Bonafonte A. Bilingual prosodic dataset compilation for spoken language translation. Paper presented at: IberSPEECH'18; 2018 Nov 21-23; Barcelona, Spain. http://hdl.handle.net/10230/35600
http://hdl.handle.net/10230/35600
This dataset is licensed under a Creative Commons licence. This license doesn't affect to file's content which can be protected by copyright.
https://creativecommons.org/licenses/by-sa/4.0/
info:eu-repo/semantics/openAccess
Heroes Corpus
info:eu-repo/semantics/other
Dataset
Parallel bilingual speech corpus prosody
oai:repositori.upf.edu:10230/370902020-10-27T11:28:40Zcom_10230_5963col_10230_24646
Bas Villalba, Jesús Antonio
Sebastián Gallés, Núria
2019-04-11T07:42:31Z
2019-04-11T07:42:31Z
2019-04-09
http://hdl.handle.net/10230/37090
Data in CSV. (177 MB). User guide can be found in paper's supporting information
Infants' raw eye-tracker data used in the study presented at the paper entitled "Infants' representation of social hierarchies in absence of physical dominance"
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2019-04-11T07:42:31Z
No. of bitstreams: 1
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Raw_Data_Infants.csv: 76431360 bytes, checksum: f2c04b3105f7b50ab0ee75a13d1348fe (MD5)
eng
Creative Commons Attribution 3.0 Spain (CC BY 3.0 ES)
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
Raw Data Infants' representation of social hierarchies in absence of physical dominance
info:eu-repo/semantics/other
Dataset
Raw eye-tracker data
oai:repositori.upf.edu:10230/372912019-05-24T08:57:45Zcom_10230_5963col_10230_24646
Bas Villalba, Jesús Antonio
Sebastián Gallés, Núria
2019-05-24T08:43:35Z
2019-05-24T08:43:35Z
2019-05-24
Bas Villalba JA, Sebastián Gallés N. Raw Data Influence of agents' social status on 18-to-21-month-old infants' learning [dataset]. Repositori Digital de la UPF: Barcelona; 2019. Disponible a: http://hdl.handle.net/10230/37291
http://hdl.handle.net/10230/37291
Data in CSV. (104,9 MB). User guide can be found in paper's supporting information.
Infants' raw eye-tracker data used in the study presented at the paper entitled "Influence of agents' social status on 18-to-21-month-old infants' learning".
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2019-05-24T08:43:35Z
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Raw_Data_Influence_of_agents'_social_status_on_18-to-21-month-old_infants'_learning.csv: 98306433 bytes, checksum: a8878e63b0a5b21d61a5ff837e4d5451 (MD5)
eng
Universitat Pompeu Fabra
Licensed under a Creative Commons License (CC-BY)
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
Raw Data Influence of agents' social status on 18-to-21-month-old infants' learning
info:eu-repo/semantics/other
Dataset
Raw eye-tracker data
oai:repositori.upf.edu:10230/427642021-02-16T09:13:10Zcom_10230_5963col_10230_24646
Leguia, Marc G.
Martínez, Cristina G. B.
Malvestio, Irene
Tauste Campo, Adrià
Rocamora, Rodrigo
Levnajić, Zoran
Andrzejak, Ralph Gregor
2019-11-06T12:08:03Z
2019-11-06T12:08:03Z
2019
Leguia MG, Martínez CGB, Malvestio I, Tauste Campo A, Rocamora R, Levnajić Z, Andrzejak RG. Inferring directed networks using a rank-based connectivity measure [software]. Repositori Digital de la UPF: Barcelona; 2019. Available from: http://hdl.handle.net/10230/42764
http://hdl.handle.net/10230/42764
MATLAB source codes (.m) and README (.pdf)
This page provides the source code underlying the manuscript:
Leguia MG, Martínez CGB, Malvestio I, Campo A T, Rocamora R, Levnajic Z,
Andrzejak RG. 2019. Inferring directed networks using a rank-based connectivity
measure. Phys. Rev. E. 99, 012319
If you use any of these resources, please make sure that you cite this reference. For
more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2019-11-06T12:08:03Z
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IntegratorNetnoise.m: 1797 bytes, checksum: 6a24c84cd4ac2258660ebfadb0ff3ce0 (MD5)
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LorenzLorenzODENetdifR.m: 705 bytes, checksum: 7559693d062afe168cdc64044bb61d95 (MD5)
This work was funded by the EU via H2020 Marie Sklodowska-Curie project COSMOS, Grant No. 642563 (M.G.L., I.M., Z.L., and R.G.A). R.G.A. and C.G.B.M. acknowledge funding from the Spanish Ministry of Economy and Competitiveness (Grant No. FIS2014-54177-R) and the CERCA Programme/Generalitat de Catalunya. C.G.B.M. acknowledges the support by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (Grant No. MDM-2015-0502).
application/pdf
eng
Universitat Pompeu Fabra
Publicació relacionada: Leguia MG, Martínez CGB, Malvestio I, Tauste Campo A, Rocamora R, Levnajić Z, Andrzejak RG. Inferring directed networks using a rank-based connectivity measure. Phys Rev E.2019 Jan 22;99(1):012319. DOI: 10.1103/PhysRevE.99.012319 http://hdl.handle.net/10230/41825
http://hdl.handle.net/10230/41825
info:eu-repo/grantAgreement/EC/H2020/642563
info:eu-repo/grantAgreement/ES/1PE/FIS2014-54177-R
Licensed under a Creative Commons License (CC-BY) 4.0
The source codes, data and results on these sites are free of charge for research and education purposes only. Any commercial or military use is prohibited. All resources are provided without any expressed or implied warranty. In no event the authors of the article or any of their host institutions are liable for any damages arising from the use of the software, data or results.
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Chaotic systems
Collective dynamics
Directed networks
Electroencephalography
Nonlinear dynamics
Networks
Inferring directed networks using a rank-based connectivity measure [software]
info:eu-repo/semantics/other
Software
oai:repositori.upf.edu:10230/428272021-02-03T11:10:56Zcom_10230_5963col_10230_24646
Malvestio, Irene
Kreuz, Thomas
Andrzejak, Ralph Gregor
2019-11-12T12:09:51Z
2019-11-12T12:09:51Z
2017
Malvestio I, Kreuz T, Andrzejak RG. Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains [software]. Repositori Digital de la UPF: Barcelona; 2017. Available from: http://hdl.handle.net/10230/42827
http://hdl.handle.net/10230/42827
MATLAB source codes (.m), MATLAB workspace (.mat) and README (.pdf)
This page provides the source code and results underlying the manuscript:
Malvestio I, Kreuz T, Andrzejak RG. 2017. Robustness and versatility of a nonlinear
interdependence method for directional coupling detection from spike trains. Physical
Review E 96, 022203
If you use any of these resources, please make sure that you cite this reference. For
more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2019-11-12T12:09:51Z
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MalvestioPRE2017Example.m: 2641 bytes, checksum: 0064ebb1092eaeac5280bb13519b4b26 (MD5)
MalvestioPRE2017Example024.mat: 12456 bytes, checksum: 4493b205cc6effb9d075e96c7d4014f2 (MD5)
MalvestioPRE2017FromHtoL.m: 2044 bytes, checksum: b5f4764548f85b4b97230b308916c8ed (MD5)
MalvestioPRE2017Hpointprocess.m: 3853 bytes, checksum: eb199cd5caf09861bfcd7c6ecd27264e (MD5)
SpikeTrainSet.m: 46413 bytes, checksum: 803e0a383fc89f5688e23b4919ec077e (MD5)
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MalvestioPRE2017Hpointprocess.m: 3853 bytes, checksum: eb199cd5caf09861bfcd7c6ecd27264e (MD5)
SpikeTrainSet.m: 46413 bytes, checksum: 803e0a383fc89f5688e23b4919ec077e (MD5)
We acknowledge funding from the European Union Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 642563 “Complex Oscillatory Systems: Modeling and Analysis” (COSMOS), (I.M., T.K., R.G.A.), and from the Volkswagen Foundation, the Spanish Ministry of Economy and Competitiveness Grant No. FIS2014-54177-R and the CERCA Programme of the Generalitat de Catalunya (R.G.A).
application/pdf
eng
Publicació relacionada: Malvestio I, Kreuz T, Andrzejak RG. Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains. Phys. Rev. E. 2017 Aug 3;96:022203. DOI: 10.1103/PhysRevE.96.022203 http://hdl.handle.net/10230/32713
http://hdl.handle.net/10230/32713
info:eu-repo/grantAgreement/ES/1PE/FIS2014-54177-R
info:eu-repo/grantAgreement/EC/H2020/642563
Licensed under a Creative Commons License (CC-BY) 4.0
The source codes, data and results on these sites are free of charge for research and education purposes only. Any commercial or military use is prohibited. All resources are provided without any expressed or implied warranty. In no event the authors of the article or any of their host institutions are liable for any damages arising from the use of the software, data or results.
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains [software]
info:eu-repo/semantics/other
Software
Coupled oscillators
Synchronization
Chaotic systems
Dynamical systems
Neuronal network models
Time series analysis
Interdisciplinary physics
Networks
Nonlinear dynamics
oai:repositori.upf.edu:10230/428282021-02-03T11:16:45Zcom_10230_5963col_10230_24646
Andrzejak, Ralph Gregor
Ruzzene, Giulia
Malvestio, Irene
2019-11-12T12:30:33Z
2019-11-12T12:30:33Z
2017
Andrzejak RG, Ruzzene G, Malvestio I. Generalized synchronization between chimera states [software]. Repositori Digital de la UPF: Barcelona; 2017. Available from: http://hdl.handle.net/10230/42828
http://hdl.handle.net/10230/42828
Source code in MATLAB format (.m)
This page provides the source code underlying the manuscript:
Andrzejak RG, Ruzzene G, Malvestio I. 2017. Generalized synchronization between
chimera states. Chaos. 27(5): 053114
If you use any of these resources, please make sure that you cite this reference. For
more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2019-11-12T12:30:33Z
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We acknowledge funding from the Volkswagen foundation, the Spanish Ministry of Economy and Competitiveness, Grant No. FIS2014-54177-R, the CERCA Programme of the Generalitat de Catalunya (R.G.A. and G.R.), and from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 642563 (R.G.A. and I.M.).
eng
Publicació relacionada: Andrzejak RG, Ruzzene G, Malvestio I. Generalized synchronization between chimera states. Chaos. 2017;27(5):053114. DOI: 10.1063/1.4983841 http://hdl.handle.net/10230/32159
http://hdl.handle.net/10230/32159
info:eu-repo/grantAgreement/ES/1PE/FIS2014-54177-R
info:eu-repo/grantAgreement/EC/H2020/642563
Licensed under a Creative Commons License (CC-BY) 4.0
The source codes, data and results on these sites are free of charge for research and education purposes only. Any commercial or military use is prohibited. All resources are provided without any expressed or implied warranty. In no event the authors of the article or any of their host institutions are liable for any damages arising from the use of the software, data or results.
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Generalized synchronization between chimera states [software]
info:eu-repo/semantics/other
Software
Chimera states
Synchronization
Auxiliary system approach
Chaos
oai:repositori.upf.edu:10230/428292021-01-18T11:43:43Zcom_10230_5963col_10230_24646
Andrzejak, Ralph Gregor
Schindler, Kaspar A.
Rummel, Christian
2019-11-12T14:20:50Z
2019-11-12T14:20:50Z
2012
Andrzejak RG, Schindler KA, Rummel C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients [dataset]. Repositori Digital de la UPF: Barcelona; 2012. Available from: http://hdl.handle.net/10230/42829
http://hdl.handle.net/10230/42829
Zip files containing MATLAB files (.m) and TXT files (.txt).
This page provides the source code, data, and detailed results of the manuscript:
Andrzejak RG, Schindler K, Rummel C. Nonrandomness, nonlinear dependence, and
nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev.
E, 86, 046206, 2012.
If you use any of these resources, please make sure that you cite this reference. For
more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2019-11-12T14:20:50Z
No. of bitstreams: 15
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R.G.A. acknowledges grant FIS-2010-18204 of the Spanish Ministry of Education and Science.
eng
Publicació relacionada: Andrzejak RG, Schindler K, Rummel C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E. 2012;86:046206. DOI: 10.1103/PhysRevE.86.046206 http://hdl.handle.net/10230/43557
http://hdl.handle.net/10230/43557
info:eu-repo/grantAgreement/ES/3PN/FIS-2010-18204
Licensed under a Creative Commons License (CC-BY) 4.0
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients [dataset]
info:eu-repo/semantics/other
Dataset
Software
EEG download page
Electroencephalogram
Epilepsy
Intracranial EEG recordings
Nonlinear signal analysis
Nonlinear time series analysis
Free EEG database
Nonlinear prediction error source code
Surrogate signals
Surrogate source code
EEG download page Bonn
Electroencephalographic recordings
Open Matlab source codes
oai:repositori.upf.edu:10230/428942021-02-02T16:26:59Zcom_10230_5963col_10230_24646
Andrzejak, Ralph Gregor
Lehnertz, Klaus
Rieke, Christoph
Mormann, Florian
David, Peter
Elger, Christian E.
2019-11-19T12:40:29Z
2019-11-19T12:40:29Z
2001
Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, Elger CE. Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state [dataset]. Repositori Digital de la UPF: Barcelona; 2001. Available from: http://hdl.handle.net/10230/42894
http://hdl.handle.net/10230/42894
Text files (.txt). Files: For each set (A-E) there is a ZIP-file containing 100 TXT-files. Each TXT-file consists of 4096 samples of one EEG time series in ASCII code. SET A in file Z.zip containing Z000.txt - Z100.txt, SET B in file O.zip containing O000.txt - O100.txt, SET C in file N.zip containing N000.txt - N100.txt, SET D in file F.zip containing F000.txt - F100.txt, SET E in file S.zip containing S000.txt - S100.txt
This page data analyzed in the manuscript: Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, Elger CE (2001) Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Phys. Rev. E, 64, 061907. If you use any of these resources, please make sure that you cite this reference. For more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2019-11-19T12:40:29Z
No. of bitstreams: 5
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N.zip: 574452 bytes, checksum: 0bb8e39ae7530ba17f55b5b4f14e6a02 (MD5)
F.zip: 582967 bytes, checksum: 10f78c004122c609e8eef74de8790af3 (MD5)
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Z.zip: 578047 bytes, checksum: ca5c761d62704c4d2465822e2131f868 (MD5)
O.zip: 625970 bytes, checksum: 666ade7e9d519935103404d4a8d81d7d (MD5)
N.zip: 574452 bytes, checksum: 0bb8e39ae7530ba17f55b5b4f14e6a02 (MD5)
F.zip: 582967 bytes, checksum: 10f78c004122c609e8eef74de8790af3 (MD5)
S.zip: 764359 bytes, checksum: 1d560ac1e03a5c19bb7f336e270ff286 (MD5)
This work was supported by the Deutsche Forschungsgemeinschaf.
eng
Publicació relacionada: Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, Elger CE. Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys. Rev. E. 2012; 64:061907. DOI: 10.1103/PhysRevE.64.061907 http://hdl.handle.net/10230/43637
http://hdl.handle.net/10230/43637
Licensed under a Creative Commons License (CC-BY) 4.0
The source codes, data and results on these sites are free of charge for research and education purposes only. Any commercial or military use is prohibited. All resources are provided without any expressed or implied warranty. In no event the authors of the article or any of their host institutions are liable for any damages arising from the use of the software, data or results.
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state [dataset]
info:eu-repo/semantics/other
Dataset
Nonlinear time series analysis
Prediction error
Correlation dimension
Surrogates
Electroencephalographic recordings
Epilepsy
oai:repositori.upf.edu:10230/428952021-02-03T11:13:53Zcom_10230_5963col_10230_24646
Chicharro Raventós, Daniel
Andrzejak, Ralph Gregor
2019-11-19T13:00:28Z
2019-11-19T13:00:28Z
2009
Chicharro D, Andrzejak RG. Reliable detection of directional couplings using rank statistics [software]. Repositori Digital de la UPF; 2009. Available from: http://hdl.handle.net/10230/42895
http://hdl.handle.net/10230/42895
Source code in MATLAB format (.m)
This page provides the source code underlying the manuscript:
Chicharro D, Andrzejak RG (2009): Reliable detection of directional couplings using
rank statistics. Physical Review E, 80, 026217.
If you use any of these resources, please make sure that you cite this reference. For
more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2019-11-19T13:00:28Z
No. of bitstreams: 1
HSLMNCom.m: 5912 bytes, checksum: f050f0512191ae2b38304ced2c4d6a56 (MD5)
Made available in DSpace on 2019-11-19T13:00:28Z (GMT). No. of bitstreams: 1
HSLMNCom.m: 5912 bytes, checksum: f050f0512191ae2b38304ced2c4d6a56 (MD5)
D.C. was supported by Grant No. 2008FI-B 00460 of the “Generalitat de Catalunya” and European Social Funds. R.G.A. acknowledges Grant No. BFU2007-61710 of the Spanish Ministry of Education and Science.
eng
Publicació relacionada: Chicharro D, Andrzejak RG. Reliable detection of directional couplings using rank statistics. Physical Review E. 2009; 80(2), 026217: 1-5. DOI/n10.1103/PhysRevE.80.026217 http://hdl.handle.net/10230/16204
http://hdl.handle.net/10230/16204
info:eu-repo/grantAgreement/ES/2PN/BFU2007-61710
Licensed under a Creative Commons License (CC-BY) 4.0
The source codes, data and results on these sites are free of charge for research and education purposes only. Any commercial or military use is prohibited. All resources are provided without any expressed or implied warranty. In no event the authors of the article or any of their host institutions are liable for any damages arising from the use of the software, data or results.
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Reliable detection of directional couplings using rank statistics [software]
info:eu-repo/semantics/other
Software
Nonlinear time series analysis
Nonlinear interdependence
Synchronization
oai:repositori.upf.edu:10230/428962021-02-03T11:18:34Zcom_10230_5963col_10230_24646
Andrzejak, Ralph Gregor
Ledberg, Anders
Deco, Gustavo
2019-11-19T13:55:28Z
2019-11-19T13:55:28Z
2006
Andrzejak RG, Ledberg A, Deco G. Detecting event-related time-dependent directional couplings [software]. Repositori Digital de la UPF; 2006. Available from: http://hdl.handle.net/10230/42896
http://hdl.handle.net/10230/42896
Source code in MATLAB format (.m)
This page provides the source code underlying the manuscript:
Andrzejak RG, Ledberg A, Deco G (2006): Detecting event-related time-dependent
directional couplings. New Journal of Physics 8, 6
If you use any of these resources, please make sure that you cite this reference. For
more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2019-11-19T13:55:28Z
No. of bitstreams: 1
Hi.m: 3489 bytes, checksum: c147e45036afb3c902b42b1d440ea843 (MD5)
Made available in DSpace on 2019-11-19T13:55:28Z (GMT). No. of bitstreams: 1
Hi.m: 3489 bytes, checksum: c147e45036afb3c902b42b1d440ea843 (MD5)
RGA was supported by a Feodor Lynen-fellowship of the Alexander von Humboldt-Foundation.
eng
Publicació relacionada: Andrzejak RG, Ledberg A, Deco G. Detecting event-related time-dependent directional couplings. New J. Phys. 2006;8:6. DOI: 10.1088/1367-2630/8/1/006
http://dx.doi.org/10.1088/1367-2630/8/1/006
Licensed under a Creative Commons License (CC-BY) 4.0
The source codes, data and results on these sites are free of charge for research and education purposes only. Any commercial or military use is prohibited. All resources are provided without any expressed or implied warranty. In no event the authors of the article or any of their host institutions are liable for any damages arising from the use of the software, data or results.
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Detecting event-related time-dependent directional couplings [software]
info:eu-repo/semantics/other
Software
Nonlinear time series analysis
Nonlinear interdependence
Event-related dynamics
oai:repositori.upf.edu:10230/429392021-02-03T11:19:27Zcom_10230_5963col_10230_24646
Andrzejak, Ralph Gregor
Kreuz, Thomas
2019-11-22T11:10:18Z
2019-11-22T11:10:18Z
2011
Andrzejak RG, Kreuz T. Characterizing unidirectional couplings between point processes and flows [dataset]. Repositori Digital de la UPF: Barcelona; 2011. Available from: http://hdl.handle.net/10230/42939
http://hdl.handle.net/10230/42939
MATLAB source codes (.m), MATLAB workspace (.mat) and README (.pdf)
This page provides the source code and results underlying the manuscript:
Andrzejak RG, Kreuz T (2011): Characterizing unidirectional couplings between point processes
and flows. EPL, 96, 50012
If you use any of these resources, please make sure that you cite this reference. For
more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2019-11-22T11:10:18Z
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AndrzejakKreuzExampleData.mat: 1536063 bytes, checksum: a2560e3ee2be84e2d7c0479cd2db329c (MD5)
AndrzejakKreuzExample.m: 1984 bytes, checksum: fe63c0c85a3b5eaefc7bef3a6de3c5b1 (MD5)
AndrzejakKreuzL.m: 2256 bytes, checksum: 2ca45332b09b3c4d6ec2b8c84cf8b480 (MD5)
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f_SPIKE_ISI_distance_new.m: 43314 bytes, checksum: 8c61c3a7e6a42663b601768884d10961 (MD5)
RGA acknowledges grant FIS-2010-18204 of the Spanish Ministry of Education and Science.
application/pdf
eng
Publicació relacionada: Andrzejak RG, Kreuz T. Characterizing unidirectional couplings between point processes and flows. Europhys Lett. 2011; 96(5):50012. DOI: 10.1209/0295-5075/96/50012
http://dx.doi.org/10.1209/0295-5075/96/50012
info:eu-repo/grantAgreement/ES/3PN/FIS-2010-18204
Licensed under a Creative Commons License (CC-BY) 4.0
The source codes, data and results on these sites are free of charge for research and education purposes only. Any commercial or military use is prohibited. All resources are provided without any expressed or implied warranty. In no event the authors of the article or any of their host institutions are liable for any damages arising from the use of the software, data or results.
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Characterizing unidirectional couplings between point processes and flows [dataset]
info:eu-repo/semantics/other
Dataset
Software
Nonlinear time series analysis
Nonlinear interdependence
Point processes
Neuronal spiking
oai:repositori.upf.edu:10230/429402021-02-03T11:09:05Zcom_10230_5963col_10230_24646
Naro, Daniel
Rummel, Christian
Schindler, Kaspar A.
Andrzejak, Ralph Gregor
2019-11-22T11:43:15Z
2019-11-22T11:43:15Z
2014
Naro D, Rummel C, Schindler K, Andrzejak RG. Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score [software]. Repositori Digital de la UPF: Barcelona; 2014. Available from: http://hdl.handle.net/10230/42940
http://hdl.handle.net/10230/42940
MATLAB source codes (.m) and MATLAB workspace (.mat)
This page provides the source code and results underlying the manuscript:
Naro D, Rummel C, Schindler K, Andrzejak RG (2014): Detecting determinism with improved
sensitivity in time series: Rank-based nonlinear predictability score. Phys. Rev. E. 90:032913
If you use any of these resources, please make sure that you cite this reference. For
more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2019-11-22T11:43:15Z
No. of bitstreams: 2
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NaroRummelSchindlerAndrzejak.m: 9251 bytes, checksum: 57018a6d70238b4366af3135a1067ea0 (MD5)
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NaroRummelSchindlerAndrzejak.m: 9251 bytes, checksum: 57018a6d70238b4366af3135a1067ea0 (MD5)
R.G.A. acknowledges Grant No. FIS-2010-18204 of the Spanish Ministry of Education and Science and funding from the Volkswagen Foundation.
eng
Publicació relacionada: Naro D, Rummel C, Schindler K, Andrzejak RG. Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score. Phys Rev E. 2014;90(3):032913. DOI: 10.1103/PhysRevE.90.032913 http://hdl.handle.net/10230/43638
http://hdl.handle.net/10230/43638
info:eu-repo/grantAgreement/ES/3PN/FIS-2010-18204
Licensed under a Creative Commons License (CC-BY) 4.0
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score [software]
info:eu-repo/semantics/other
Nonlinear time series analysis
Prediction error
Electroencephalographic recordings
Epilepsy
oai:repositori.upf.edu:10230/429412021-02-03T11:17:43Zcom_10230_5963col_10230_24646
Andrzejak, Ralph Gregor
Mormann, Florian
Kreuz, Thomas
2019-11-22T12:21:47Z
2019-11-22T12:21:47Z
2014
Andrzejak RG, Mormann F, Kreuz T. Detecting determinism from point processes [software]. Repositori Digital de la UPF: Barcelona; 2014. Available from: http://hdl.handle.net/10230/42941
http://hdl.handle.net/10230/42941
Source code in MATLAB format (.m)
This page provides the source code underlying the manuscript:
Andrzejak RG, Mormann F, Kreuz T. 2014. Detecting determinism from point
processes. Phys. Rev. E. 90, 062906
If you use any of these resources, please make sure that you cite this reference. For
more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2019-11-22T12:21:47Z
No. of bitstreams: 1
Andrzejak_PRE_90_062906_SourceCodes.zip: 24879 bytes, checksum: 8b6968fb6521abe2936879081505066e (MD5)
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Andrzejak_PRE_90_062906_SourceCodes.zip: 24879 bytes, checksum: 8b6968fb6521abe2936879081505066e (MD5)
R.G.A. acknowledges Grant No. FIS-2010-18204 of the Spanish Ministry of Education and Science. R.G.A. and F.M. acknowledge funding from the Volkswagen Foundation.
eng
Publicació relacionada: Andrzejak RG, Mormann F, Kreuz T. Detecting determinism from point processes. Phys Rev E. 2014;90:062906. DOI: 10.1103/PhysRevE.90.062906 http://hdl.handle.net/10230/43555
http://hdl.handle.net/10230/43555
info:eu-repo/grantAgreement/ES/3PN/FIS-2010-18204
Licensed under a Creative Commons License (CC-BY) 4.0
The source codes, data and results on these sites are free of charge for research and education purposes only. Any commercial or military use is prohibited. All resources are provided without any expressed or implied warranty. In no event the authors of the article or any of their host institutions are liable for any damages arising from the use of the software, data or results.
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Detecting determinism from point processes [software]
info:eu-repo/semantics/other
Software
Nonlinear time series analysis
Prediction error
Point processes
Neuronal spiking
oai:repositori.upf.edu:10230/453172021-03-29T14:15:43Zcom_10230_5963col_10230_24646
González Martínez, Cristina
2020-09-21T09:59:26Z
2020-09-21T09:59:26Z
2020
González Martínez C. Public database containing micro and macro electroencephalographic recordings from epilepsy patients [dataset]. Repositori Digital de la UPF: Barcelona; 2020. Available from: http://hdl.handle.net/10230/45317
http://hdl.handle.net/10230/45317
Zip files containing TXT files (.txt), MATLAB
This page provides the data of the manuscript:
Martínez, C. G. B., Niediek, J., Mormann, F. & Andrzejak,R. G. Seizure onset zone
lateralization using a nonlinear analysis of micro versus macro
electroencephalographic recordings during seizure-free stages of the sleep-wake cycle
from epilepsy patients. Frontiers in Neurology 11, 1057, 2020.
If you use any of this data, please make sure that you cite this reference. For more
detailed information, please refer to https://www.upf.edu/web/ntsa/downloads
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2020-09-21T09:59:26Z
No. of bitstreams: 3
Signals_2020_mat.zip: 1653445731 bytes, checksum: f9d7b825171f546715a33d878a781ca3 (MD5)
Signals_2020_txt.zip: 1858351480 bytes, checksum: 41c10a9f45bc20234e43766a56e6e041 (MD5)
readme.txt: 2757 bytes, checksum: adb8aa8a56121c8376ce94d276824574 (MD5)
Made available in DSpace on 2020-09-21T09:59:26Z (GMT). No. of bitstreams: 3
Signals_2020_mat.zip: 1653445731 bytes, checksum: f9d7b825171f546715a33d878a781ca3 (MD5)
Signals_2020_txt.zip: 1858351480 bytes, checksum: 41c10a9f45bc20234e43766a56e6e041 (MD5)
readme.txt: 2757 bytes, checksum: adb8aa8a56121c8376ce94d276824574 (MD5)
C.G.B.M acknowledges grant FIS2014-54177-R of the Spanish Ministry of Education and Science and grant , MDM-2015-0502 of the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme.
eng
Universitat Pompeu Fabra
Publicació relacionada: Martínez CGB, Niediek J, Mormann F, Andrzejak RG. Seizure onset zone lateralization using a non-linear analysis of micro vs. macro electroencephalographic recordings during seizure-free stages of the sleep-wake cycle from epilepsy patients. Front Neurol. 2020 Sep 17;11:553885. http:dx.doi.org/10.3389/fneur.2020.553885 http://hdl.handle.net/10230/46087
http://hdl.handle.net/10230/46087
info:eu-repo/grantAgreement/ES/1PE/FIS2014-54177-R
Licensed under a Creative Commons License (CC-BY) 4.0
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Public database containing micro and macro electroencephalographic recordings from epilepsy patients
info:eu-repo/semantics/other
Dataset
Electroencephalogram
Epilepsy
Intracranial EEG recordings
Free EEG database
Electroencephalographic recordings
oai:repositori.upf.edu:10230/462092021-02-03T11:15:20Zcom_10230_5963col_10230_24646
Andrzejak, Ralph Gregor
Ruzzene, Giulia
Schöll, Eckehard
Omelchenko, Iryna
2021-01-19T08:24:20Z
2021-01-19T08:24:20Z
2020
Andrzejak RG, Ruzzene G, Schöll E, Omelchenko I. Two populations of coupled quadratic maps exhibit a plentitude of symmetric and symmetry broken dynamics [source code and workspaces]. Repositori Digital de la UPF: Barcelona; 2021. Available from: http://hdl.handle.net/10230/46209
http://hdl.handle.net/10230/46209
MATLAB source code (.m) and MATLAB data (.mat)
This page provides the source code and results underlying the manuscript:
Andrzejak RG, Ruzzene G, Schöll E, Omelchenko I (2020) Two populations of coupled
quadratic maps exhibit a plentitude of symmetric and symmetry broken dynamics.
Chaos, 30, 033125
If you use any of these resources, please make sure that you cite this reference. For
more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads
Submitted by Natàlia PLANCHERIA ROCA (natalia.plancheria@upf.edu) on 2021-01-19T08:24:20Z
No. of bitstreams: 1
NewAndrzejakChaos2020All.zip: 23187 bytes, checksum: 3dbe8b09bdbf88b696d2a3dd32fcdcb7 (MD5)
Made available in DSpace on 2021-01-19T08:24:20Z (GMT). No. of bitstreams: 1
NewAndrzejakChaos2020All.zip: 23187 bytes, checksum: 3dbe8b09bdbf88b696d2a3dd32fcdcb7 (MD5)
We acknowledge funding from the Spanish Ministry of Economy and Competitiveness under Grant No. FIS2014-54177-R (R.G.A. and G.R.), the CERCA Programme of the Generalitat de Catalunya (R.G.), and the Deutsche Forschungsgemeinschaft (DFG) under Project No. 163436311-SFB 910 (E.S. and I.O.).
eng
Universitat Pompeu Fabra
Publicació relacionada: Andrzejak RG, Ruzzene G, Schöll E, Omelchenko I. Two populations of coupled quadratic maps exhibit a plentitude of symmetric and symmetry broken dynamics. Chaos. 2020 Mar 17;30(3):033125. DOI: 10.1063/5.0002272 http://hdl.handle.net/10230/43960
http://hdl.handle.net/10230/43960
info:eu-repo/grantAgreement/ES/1PE/FIS2014-54177-R
Licensed under a Creative Commons License (CC-BY) 4.0
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Two populations of coupled quadratic maps exhibit a plentitude of symmetric and symmetry broken dynamics [source code and workspaces]
info:eu-repo/semantics/other
Dataset
Software
Chimera states
Coupled maps
Fractals
Mandelbrot set
Chaos