{"id":"https://openalex.org/W3009417347","doi":"https://doi.org/10.1109/icassp40776.2020.9053790","title":"Overdetermined Independent Vector Analysis","display_name":"Overdetermined Independent Vector Analysis","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3009417347","doi":"https://doi.org/10.1109/icassp40776.2020.9053790","mag":"3009417347"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9053790","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053790","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2003.02458","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084590882","display_name":"Rintaro Ikeshita","orcid":"https://orcid.org/0000-0003-2608-1999"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Rintaro Ikeshita","raw_affiliation_strings":["NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan","NTT Corporation,NTT Communication Science Labs.,Kyoto,Japan"],"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"NTT Corporation,NTT Communication Science Labs.,Kyoto,Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021240106","display_name":"Tomohiro Nakatani","orcid":"https://orcid.org/0000-0002-7487-7150"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomohiro Nakatani","raw_affiliation_strings":["NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan","NTT Corporation,NTT Communication Science Labs.,Kyoto,Japan"],"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"NTT Corporation,NTT Communication Science Labs.,Kyoto,Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009309584","display_name":"Shoko Araki","orcid":"https://orcid.org/0000-0003-4363-4305"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shoko Araki","raw_affiliation_strings":["NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan","NTT Corporation,NTT Communication Science Labs.,Kyoto,Japan"],"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"NTT Corporation,NTT Communication Science Labs.,Kyoto,Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5084590882"],"corresponding_institution_ids":["https://openalex.org/I2251713219"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02041484,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"591","last_page":"595"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":1.0,"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/T11447","display_name":"Blind Source Separation Techniques","score":1.0,"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/T10860","display_name":"Speech and Audio Processing","score":0.9939000010490417,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/overdetermined-system","display_name":"Overdetermined system","score":0.8475981950759888},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.6376065611839294},{"id":"https://openalex.org/keywords/independent-component-analysis","display_name":"Independent component analysis","score":0.6250166296958923},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6228366494178772},{"id":"https://openalex.org/keywords/orthogonality","display_name":"Orthogonality","score":0.5989970564842224},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5604725480079651},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5235109925270081},{"id":"https://openalex.org/keywords/blind-signal-separation","display_name":"Blind signal separation","score":0.5172513127326965},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4692649245262146},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4507952034473419},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.44072994589805603},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4323243498802185},{"id":"https://openalex.org/keywords/coordinate-descent","display_name":"Coordinate descent","score":0.4322817325592041},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.35475829243659973},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.296461284160614},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.23401153087615967},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1720277965068817},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11781737208366394}],"concepts":[{"id":"https://openalex.org/C81901731","wikidata":"https://www.wikidata.org/wiki/Q331348","display_name":"Overdetermined system","level":2,"score":0.8475981950759888},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.6376065611839294},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.6250166296958923},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6228366494178772},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.5989970564842224},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5604725480079651},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5235109925270081},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.5172513127326965},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4692649245262146},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4507952034473419},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.44072994589805603},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4323243498802185},{"id":"https://openalex.org/C157553263","wikidata":"https://www.wikidata.org/wiki/Q5168004","display_name":"Coordinate descent","level":2,"score":0.4322817325592041},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35475829243659973},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.296461284160614},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.23401153087615967},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1720277965068817},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11781737208366394},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/icassp40776.2020.9053790","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053790","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2003.02458","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.02458","pdf_url":"https://arxiv.org/pdf/2003.02458","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2003.02458","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2003.02458","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"},{"id":"doi:10.17023/trcm-3n80","is_oa":true,"landing_page_url":"https://doi.org/10.17023/trcm-3n80","pdf_url":null,"source":{"id":"https://openalex.org/S7407051697","display_name":"IEEE RESOURCE CENTERS","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"},{"id":"mag:3009417347","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2003.02458","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.02458","pdf_url":"https://arxiv.org/pdf/2003.02458","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W108815450","https://openalex.org/W1543386260","https://openalex.org/W1876052200","https://openalex.org/W1992080197","https://openalex.org/W2019502123","https://openalex.org/W2042567328","https://openalex.org/W2048430129","https://openalex.org/W2072548008","https://openalex.org/W2082612735","https://openalex.org/W2099741732","https://openalex.org/W2114461480","https://openalex.org/W2119647652","https://openalex.org/W2124195644","https://openalex.org/W2127851351","https://openalex.org/W2128447814","https://openalex.org/W2133069808","https://openalex.org/W2141224535","https://openalex.org/W2151834013","https://openalex.org/W2154167944","https://openalex.org/W2158035059","https://openalex.org/W2158143227","https://openalex.org/W2170768669","https://openalex.org/W2245569228","https://openalex.org/W2408629183","https://openalex.org/W2765844457","https://openalex.org/W2951431217","https://openalex.org/W2952350176","https://openalex.org/W2997453778","https://openalex.org/W2997688633","https://openalex.org/W4238957295","https://openalex.org/W6604498989","https://openalex.org/W6632571750","https://openalex.org/W6679756777","https://openalex.org/W6690610466","https://openalex.org/W6714614974"],"related_works":["https://openalex.org/W3015564447","https://openalex.org/W2911500750","https://openalex.org/W2552931705","https://openalex.org/W2111895931","https://openalex.org/W2892192795","https://openalex.org/W2950946721","https://openalex.org/W3203821075","https://openalex.org/W3105814329","https://openalex.org/W2753751479","https://openalex.org/W2769885179","https://openalex.org/W248610585","https://openalex.org/W2962585915","https://openalex.org/W2951660775","https://openalex.org/W2896517425","https://openalex.org/W3128662237","https://openalex.org/W2464779257","https://openalex.org/W2809710126","https://openalex.org/W2187507649","https://openalex.org/W3004791189","https://openalex.org/W2615883342"],"abstract_inverted_index":{"We":[0,101,179],"address":[1],"the":[2,9,14,51,60,94,98,117,122,126,139,150,155,158,188,197,209,214,219,233],"convolutive":[3],"blind":[4],"source":[5],"separation":[6],"problem":[7,52,176],"for":[8,228],"(over-)determined":[10],"case":[11],"where":[12],"(i)":[13],"number":[15],"of":[16,24,75,86,96,157,213],"nonstationary":[17,64],"target-sources":[18],"K":[19,35,62,79,229],"is":[20,131],"less":[21],"than":[22,204],"that":[23,39,121,181,196],"microphones":[25],"M,":[26],"and":[27,58,128,147,154,193],"(ii)":[28],"there":[29],"are":[30,202],"up":[31],"to":[32,42,105,132,163,218],"M":[33,56],"-":[34],"stationary":[36],"Gaussian":[37],"noises":[38],"need":[40],"not":[41,143],"be":[43,133],"extracted.":[44],"Independent":[45],"vector":[46],"analysis":[47,92],"(IVA)":[48],"can":[49],"solve":[50],"by":[53,115],"separating":[54],"into":[55],"sources":[57,153],"selecting":[59],"top":[61],"highly":[63],"signals":[65,130],"among":[66],"them,":[67],"but":[68],"this":[69],"approach":[70],"suffers":[71],"from":[72],"a":[73,175,224],"waste":[74],"computation":[76],"especially":[77],"when":[78],"\u226a":[80],"M.":[81],"Channel":[82],"reductions":[83],"in":[84],"preprocessing":[85],"IVA":[87,104,190],"by,":[88],"e.g.,":[89],"principle":[90],"component":[91],"have":[93],"risk":[95],"removing":[97],"target":[99,127],"signals.":[100],"here":[102],"extend":[103],"resolve":[106],"these":[107],"issues.":[108],"One":[109],"such":[110,183],"extension":[111],"has":[112],"been":[113],"attained":[114],"assuming":[116],"orthogonality":[118],"constraint":[119],"(OC)":[120],"sample":[123],"correlation":[124],"between":[125,152],"noise":[129],"zero.":[134],"The":[135],"proposed":[136],"IVA,":[137],"on":[138,145,169],"other":[140,198],"hand,":[141],"does":[142],"rely":[144],"OC":[146],"exploits":[148],"only":[149],"independence":[151],"stationarity":[156],"noises.":[159],"This":[160],"enables":[161],"us":[162],"develop":[164],"several":[165],"efficient":[166],"algorithms":[167,201,216],"based":[168],"block":[170],"coordinate":[171],"descent":[172],"methods":[173],"with":[174,187,191],"specific":[177],"acceleration.":[178],"clarify":[180],"one":[182],"algorithm":[184,226],"exactly":[185],"coincides":[186],"conventional":[189,220],"OC,":[192],"also":[194],"explain":[195],"newly":[199],"developed":[200],"faster":[203],"it.":[205],"Experimental":[206],"results":[207],"show":[208],"improved":[210],"computational":[211],"load":[212],"new":[215,225],"compared":[217],"methods.":[221],"In":[222],"particular,":[223],"specialized":[227],"=":[230],"1":[231],"outperforms":[232],"others.":[234]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}