{"id":"https://openalex.org/W3097445713","doi":"https://doi.org/10.21437/interspeech.2020-2388","title":"Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and its Application to Speaker Stream Separation","display_name":"Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and its Application to Speaker Stream Separation","publication_year":2020,"publication_date":"2020-10-25","ids":{"openalex":"https://openalex.org/W3097445713","doi":"https://doi.org/10.21437/interspeech.2020-2388","mag":"3097445713"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2020-2388","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2020-2388","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2020","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/A5069398831","display_name":"Keisuke Kinoshita","orcid":"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 Corporation, Japan,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Corporation, Japan,","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015424386","display_name":"Thilo von Neumann","orcid":"https://orcid.org/0000-0002-7717-8670"},"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":"Thilo von Neumann","raw_affiliation_strings":["Paderborn University, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Paderborn University, 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"},"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 Corporation, Japan,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Corporation, 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 Corporation, Japan,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Corporation, Japan,","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082075598","display_name":"Reinhold Haeb\u2010Umbach","orcid":"https://orcid.org/0000-0001-9468-7330"},"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":"Reinhold Haeb-Umbach","raw_affiliation_strings":["Paderborn University, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Paderborn University, Germany","institution_ids":["https://openalex.org/I206945453"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2652","last_page":"2656"},"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9995999932289124,"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":0.9993000030517578,"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/computer-science","display_name":"Computer science","score":0.7796210050582886},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.6619704365730286},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.595903217792511},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.5158228278160095},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4852035343647003},{"id":"https://openalex.org/keywords/separation","display_name":"Separation (statistics)","score":0.47776901721954346},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4597944915294647},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41797545552253723},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17623451352119446},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.140509694814682},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10269880294799805},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.08821693062782288},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.0783882737159729}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7796210050582886},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.6619704365730286},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.595903217792511},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.5158228278160095},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4852035343647003},{"id":"https://openalex.org/C2776061190","wikidata":"https://www.wikidata.org/wiki/Q7451805","display_name":"Separation (statistics)","level":2,"score":0.47776901721954346},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4597944915294647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41797545552253723},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17623451352119446},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.140509694814682},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10269880294799805},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.08821693062782288},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0783882737159729}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/interspeech.2020-2388","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2020-2388","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2020","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5199999809265137,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2127851351","https://openalex.org/W2221409856","https://openalex.org/W2460742184","https://openalex.org/W2734774145","https://openalex.org/W2889381673","https://openalex.org/W2891054259","https://openalex.org/W2952218014","https://openalex.org/W2952752702","https://openalex.org/W2963574857","https://openalex.org/W2963843276","https://openalex.org/W2995166068","https://openalex.org/W3124972797","https://openalex.org/W3185109982"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W3032952384","https://openalex.org/W3017902212","https://openalex.org/W2964335273","https://openalex.org/W2369669030","https://openalex.org/W1969305350"],"abstract_inverted_index":{"Recently,":[0],"the":[1,41,77,110,117,120,141,147,159,163,170,195],"source":[2,11],"separation":[3,12],"performance":[4],"was":[5],"greatly":[6],"improved":[7],"by":[8,83,135],"time-domain":[9],"audio":[10],"based":[13],"on":[14],"dualpath":[15],"recurrent":[16],"neural":[17],"network":[18],"(DPRNN).DPRNN":[19],"is":[20,32,54,70,123,133],"a":[21,27,37,75,99,103,114,136,156],"simple":[22],"but":[23],"effective":[24],"model":[25,151,190],"for":[26],"long":[28],"sequential":[29,38],"data.While":[30],"DPRNN":[31,198],"quite":[33],"efficient":[34],"in":[35,73,113,201],"modeling":[36],"data":[39,112,122],"of":[40,43,67,79,106,131],"length":[42],"an":[44],"utterance,":[45],"i.e.,":[46],"about":[47],"5":[48],"to":[49,56,59],"10":[50],"second":[51],"data,":[52],"it":[53,58,193],"harder":[55],"apply":[57],"longer":[60],"sequences":[61],"such":[62,74,174],"as":[63,175],"whole":[64],"conversations":[65],"consisting":[66],"multiple":[68],"utterances.It":[69],"simply":[71],"because,":[72],"case,":[76],"number":[78],"time":[80],"steps":[81],"consumed":[82],"its":[84],"internal":[85],"module":[86],"called":[87],"inter-chunk":[88],"RNN":[89,101,138,142,160],"becomes":[90],"extremely":[91],"large.To":[92],"mitigate":[93],"this":[94,96],"problem,":[95],"paper":[97],"proposes":[98],"multi-path":[100],"(MPRNN),":[102],"generalized":[104],"version":[105],"DPRNN,":[107],"that":[108,144,186],"models":[109],"input":[111,121],"hierarchical":[115],"manner.In":[116],"MPRNN":[118,187],"framework,":[119],"represented":[124],"at":[125],"several":[126],"(\u2265":[127],"3)":[128],"time-resolutions,":[129],"each":[130],"which":[132],"modeled":[134],"specific":[137],"sub-module.For":[139],"example,":[140],"sub-module":[143,161],"deals":[145],"with":[146],"finest":[148],"resolution":[149,166],"may":[150,167],"temporal":[152],"relationship":[153,171],"only":[154,169],"within":[155],"phoneme,":[157],"while":[158],"handling":[162],"most":[164],"coarse":[165],"capture":[168],"between":[172],"utterances":[173],"speaker":[176],"information.We":[177],"perform":[178],"experiments":[179],"using":[180],"simulated":[181],"dialogue-like":[182],"mixtures":[183],"and":[184,192],"show":[185],"has":[188],"greater":[189],"capacity,":[191],"outperforms":[194],"current":[196],"state-of-the-art":[197],"framework":[199],"especially":[200],"online":[202],"processing":[203],"scenarios.":[204]},"counts_by_year":[{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
