{"id":"https://openalex.org/W2562809169","doi":"https://doi.org/10.1109/eusipco.2016.7760655","title":"A unified Bayesian model of time-frequency clustering and low-rank approximation for multi-channel source separation","display_name":"A unified Bayesian model of time-frequency clustering and low-rank approximation for multi-channel source separation","publication_year":2016,"publication_date":"2016-08-01","ids":{"openalex":"https://openalex.org/W2562809169","doi":"https://doi.org/10.1109/eusipco.2016.7760655","mag":"2562809169"},"language":"en","primary_location":{"id":"doi:10.1109/eusipco.2016.7760655","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eusipco.2016.7760655","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 24th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073445085","display_name":"Kousuke Itakura","orcid":null},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kousuke Itakura","raw_affiliation_strings":["Graduate School of Informatics, Kyoto University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Informatics, Kyoto University, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007353694","display_name":"Yoshiaki Bando","orcid":"https://orcid.org/0000-0002-3934-0745"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshiaki Bando","raw_affiliation_strings":["Graduate School of Informatics, Kyoto University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Informatics, Kyoto University, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043140898","display_name":"Eita Nakamura","orcid":"https://orcid.org/0000-0003-4097-6027"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Eita Nakamura","raw_affiliation_strings":["Graduate School of Informatics, Kyoto University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Informatics, Kyoto University, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003325000","display_name":"Katsutoshi Itoyama","orcid":"https://orcid.org/0000-0002-7098-3896"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Katsutoshi Itoyama","raw_affiliation_strings":["Graduate School of Informatics, Kyoto University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Informatics, Kyoto University, Japan","institution_ids":["https://openalex.org/I22299242"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067956319","display_name":"Kazuyoshi Yoshii","orcid":"https://orcid.org/0000-0001-8387-8609"},"institutions":[{"id":"https://openalex.org/I22299242","display_name":"Kyoto University","ror":"https://ror.org/02kpeqv85","country_code":"JP","type":"education","lineage":["https://openalex.org/I22299242"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuyoshi Yoshii","raw_affiliation_strings":["Graduate School of Informatics, Kyoto University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Informatics, Kyoto University, Japan","institution_ids":["https://openalex.org/I22299242"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I22299242"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":null,"first_page":"2280","last_page":"2284"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.8134400844573975},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.7550915479660034},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.5923548340797424},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.584911048412323},{"id":"https://openalex.org/keywords/source-separation","display_name":"Source separation","score":0.5641207695007324},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5537652373313904},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4688699543476105},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4158988893032074},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.4139990210533142},{"id":"https://openalex.org/keywords/low-rank-approximation","display_name":"Low-rank approximation","score":0.41097259521484375},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4004800617694855},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37557584047317505},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3317905068397522},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.29571533203125},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.08852797746658325},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.08234131336212158}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.8134400844573975},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.7550915479660034},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.5923548340797424},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.584911048412323},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.5641207695007324},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5537652373313904},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4688699543476105},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4158988893032074},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.4139990210533142},{"id":"https://openalex.org/C90199385","wikidata":"https://www.wikidata.org/wiki/Q6692777","display_name":"Low-rank approximation","level":3,"score":0.41097259521484375},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4004800617694855},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37557584047317505},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3317905068397522},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.29571533203125},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.08852797746658325},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.08234131336212158},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/eusipco.2016.7760655","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eusipco.2016.7760655","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 24th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W87112183","https://openalex.org/W1521340514","https://openalex.org/W1755563775","https://openalex.org/W1880262756","https://openalex.org/W1974647903","https://openalex.org/W1987906574","https://openalex.org/W2012910847","https://openalex.org/W2031583051","https://openalex.org/W2072548008","https://openalex.org/W2074134755","https://openalex.org/W2096855653","https://openalex.org/W2104298926","https://openalex.org/W2108207895","https://openalex.org/W2113526703","https://openalex.org/W2113990625","https://openalex.org/W2127851351","https://openalex.org/W2129171989","https://openalex.org/W2167083368","https://openalex.org/W2168273590","https://openalex.org/W2205924474","https://openalex.org/W2217477878","https://openalex.org/W2913656013","https://openalex.org/W4231510805","https://openalex.org/W6631254342","https://openalex.org/W6639619044","https://openalex.org/W6677017536"],"related_works":["https://openalex.org/W2551137307","https://openalex.org/W2113526703","https://openalex.org/W2123043102","https://openalex.org/W2577807713","https://openalex.org/W2396837968","https://openalex.org/W3094316140","https://openalex.org/W2547262076","https://openalex.org/W3133205200","https://openalex.org/W2098101267","https://openalex.org/W4289363934"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,24],"statistical":[4],"method":[5,102,135],"of":[6,32,98,108,127],"multichannel":[7],"source":[8,38,75,84,99,114],"separation,":[9],"called":[10],"NMF-LDA,":[11],"that":[12,83,111,122],"unifies":[13],"nonnegative":[14],"matrix":[15,120],"factorization":[16],"(NMF)":[17],"and":[18,96,116],"latent":[19],"Dirichlet":[20],"allocation":[21],"(LDA)":[22],"in":[23,49],"hierarchical":[25],"Bayesian":[26],"manner.":[27],"If":[28],"the":[29,37,80,94,124,133],"frequency":[30],"components":[31],"sources":[33,66],"are":[34],"sparsely":[35],"distributed,":[36],"spectrograms":[39,85,115],"can":[40],"be":[41,44],"considered":[42],"to":[43,72,78,87],"disjoint":[45],"with":[46],"each":[47],"other":[48],"most":[50],"time-frequency":[51,62,109],"bins.":[52],"Under":[53],"this":[54],"assumption,":[55],"LDA":[56],"has":[57],"been":[58],"used":[59],"for":[60],"clustering":[61,107],"bins":[63,126],"into":[64],"individual":[65],"using":[67],"spatial":[68],"information.":[69],"A":[70],"way":[71],"improve":[73],"LDA-based":[74],"separation":[76],"is":[77],"consider":[79],"empirical":[81],"fact":[82],"tend":[86],"have":[88],"low-rank":[89],"structure.":[90],"To":[91],"leverage":[92],"both":[93],"sparseness":[95],"low-rankness":[97],"spectrograms,":[100],"our":[101],"iterates":[103],"an":[104,117],"LDA-step":[105],"(hard":[106],"bins)":[110],"gives":[112],"deficient":[113,125],"NMF-step":[118],"(low-rank":[119],"approximation)":[121],"completes":[123],"those":[128],"spectrograms.":[129],"Experimental":[130],"results":[131],"showed":[132],"proposed":[134],"outperformed":[136],"conventional":[137],"methods.":[138]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}