{"id":"https://openalex.org/W2891054259","doi":"https://doi.org/10.1109/icassp.2018.8462646","title":"Listening to Each Speaker One by One with Recurrent Selective Hearing Networks","display_name":"Listening to Each Speaker One by One with Recurrent Selective Hearing Networks","publication_year":2018,"publication_date":"2018-04-01","ids":{"openalex":"https://openalex.org/W2891054259","doi":"https://doi.org/10.1109/icassp.2018.8462646","mag":"2891054259"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2018.8462646","doi":"https://doi.org/10.1109/icassp.2018.8462646","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8462646","pdf_url":null,"source":{"id":"https://openalex.org/S4363607879","display_name":"IEEE International Conference on Acoustics Speech and Signal Processing","issn_l":"1520-6149","issn":["1520-6149","2379-190X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5069398831","display_name":"Keisuke Kinoshita","orcid":"https://orcid.org/0009-0008-7987-8188","observed_orcids":["https://orcid.org/0009-0008-7987-8188"]},"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":"Keisuke Kinoshita","raw_affiliation_strings":["NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011191228","display_name":"Lukas Drude","orcid":"https://orcid.org/0000-0003-3683-5432","observed_orcids":["https://orcid.org/0000-0003-3683-5432"]},"institutions":[{"id":"https://openalex.org/I206945453","display_name":"Paderborn University","ror":"https://ror.org/058kzsd48","country_code":"DE","type":"education","lineage":["https://openalex.org/I206945453"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lukas Drude","raw_affiliation_strings":["Department of Communications Engineering, Paderborn University, Paderborn, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Communications Engineering, Paderborn University, Paderborn, Germany","institution_ids":["https://openalex.org/I206945453"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023868166","display_name":"Marc Delcroix","orcid":"https://orcid.org/0000-0002-5175-7834","observed_orcids":["https://orcid.org/0000-0002-5175-7834"]},"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":"Marc Delcroix","raw_affiliation_strings":["NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021240106","display_name":"Tomohiro Nakatani","orcid":"https://orcid.org/0000-0002-7487-7150","observed_orcids":["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"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.6401,"has_fulltext":false,"cited_by_count":74,"citation_normalized_percentile":{"value":0.98609375,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"5064","last_page":"5068"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9883000254631042,"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":0.9883000254631042,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.005799999926239252,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.004000000189989805,"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/single-channel-source-separation","display_name":"single-channel source separation","score":0.9490000009536743},{"id":"https://openalex.org/keywords/recurrent-neural-networks","display_name":"recurrent neural networks","score":0.5770000219345093},{"id":"https://openalex.org/keywords/permutation-invariant-training","display_name":"permutation invariant training","score":0.6660000085830688},{"id":"https://openalex.org/keywords/deep-clustering","display_name":"deep clustering","score":0.7490000128746033}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6949018836021423},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6008186340332031},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5801018476486206},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.5615900754928589},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.5603459477424622},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5412347912788391},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5399979948997498},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.4680119752883911},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4656316637992859},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.44118624925613403},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4227503836154938},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3984178900718689},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1770254671573639},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2018.8462646","doi":"https://doi.org/10.1109/icassp.2018.8462646","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8462646","pdf_url":null,"source":{"id":"https://openalex.org/S4363607879","display_name":"IEEE International Conference on Acoustics Speech and Signal Processing","issn_l":"1520-6149","issn":["1520-6149","2379-190X"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W285277413","https://openalex.org/W1482149378","https://openalex.org/W1987906574","https://openalex.org/W2031583051","https://openalex.org/W2039844283","https://openalex.org/W2127851351","https://openalex.org/W2221409856","https://openalex.org/W2225904595","https://openalex.org/W2325237720","https://openalex.org/W2327562811","https://openalex.org/W2460742184","https://openalex.org/W2519224033","https://openalex.org/W2558649592","https://openalex.org/W2604132379","https://openalex.org/W2734774145","https://openalex.org/W2750446090","https://openalex.org/W2962728945"],"related_works":["https://openalex.org/W2768808476","https://openalex.org/W2612083379","https://openalex.org/W3094380169","https://openalex.org/W4388755868","https://openalex.org/W2408744528","https://openalex.org/W3080465232","https://openalex.org/W4312314091","https://openalex.org/W2508884455","https://openalex.org/W3107686772","https://openalex.org/W3091954663"],"abstract_inverted_index":{"Deep":[0,13,17],"learning-based":[1],"single-channel":[2],"source":[3,47,160,175,221,224],"separation":[4,48,161,222],"algorithms":[5],"are":[6],"currently":[7],"being":[8],"actively":[9],"investigated.":[10],"Among":[11],"them,":[12],"Clustering":[14],"(DC)":[15],"and":[16,40,69,107,116,139,188,213,223],"Attractor":[18],"Networks":[19],"(DANs)":[20],"have":[21,194],"made":[22],"it":[23],"possible":[24],"to":[25,45,195],"separate":[26],"an":[27,134],"arbitrary":[28,135],"number":[29,56,93,110,136,143],"of":[30,57,94,103,111,118,137,144,220],"speakers.":[31],"In":[32,203],"particular,":[33],"they":[34],"cleverly":[35],"combine":[36],"a":[37,41,78,91,125,164,172,180],"neural":[38,80,182],"network":[39,183],"K-means":[42],"clustering":[43],"algorithm":[44],"obtain":[46],"masks":[49],"with":[50,89,158],"the":[51,54,60,97,101,104,108,142,147,154,159,169,200],"assumption":[52],"that":[53,131,185],"correct":[55],"speakers":[58],"at":[59],"test":[61,148],"time":[62],"is":[63],"known":[64],"in":[65,146,211,218],"advance.":[66],"Unlike":[67],"DC":[68],"DAN,":[70],"Permutation":[71],"Invariant":[72],"Training":[73],"(PIT)":[74],"was":[75],"proposed":[76,209],"as":[77,163,171],"purely":[79,126],"network-based":[81],"mask":[82,129],"estimator.":[83],"Essentially,":[84],"however,":[85],"PIT":[86],"can":[87,132,186],"deal":[88,157],"only":[90],"fixed":[92],"speakers,":[95],"given":[96],"strong":[98],"relationship":[99],"between":[100],"dimensions":[102],"output":[105],"nodes":[106],"assumed":[109],"sources.":[112],"Considering":[113],"these":[114],"limitations":[115],"merits":[117],"such":[119],"conventional":[120,155],"methods,":[121],"this":[122,204],"paper":[123],"proposes":[124],"neural-network":[127],"based":[128,178],"estimator":[130],"handle":[133],"sources,":[138],"simultaneously":[140],"estimate":[141],"sources":[145],"signal.":[149],"To":[150],"accomplish":[151],"this,":[152],"while":[153],"methods":[156],"problem":[162,170,177],"one-pass":[165],"problem,":[166],"we":[167,206],"cast":[168],"recursive":[173],"multi-pass":[174],"extraction":[176],"on":[179,199],"recurrent":[181],"(RNN)":[184],"learn":[187],"determine":[189],"how":[190],"many":[191],"computational":[192],"steps/iterations":[193],"be":[196],"performed":[197],"depending":[198],"input":[201],"signals.":[202],"paper,":[205],"describe":[207],"our":[208],"method":[210],"detail,":[212],"experimentally":[214],"show":[215],"its":[216],"efficacy":[217],"terms":[219],"counting":[225],"performance.":[226]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":18},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2018-09-27T00:00:00"}
