{"id":"https://openalex.org/W3215005424","doi":"https://doi.org/10.1109/taslp.2021.3129363","title":"Convolutive Prediction for Monaural Speech Dereverberation and Noisy-Reverberant Speaker Separation","display_name":"Convolutive Prediction for Monaural Speech Dereverberation and Noisy-Reverberant Speaker Separation","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3215005424","doi":"https://doi.org/10.1109/taslp.2021.3129363","mag":"3215005424"},"language":"en","primary_location":{"id":"doi:10.1109/taslp.2021.3129363","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taslp.2021.3129363","pdf_url":null,"source":{"id":"https://openalex.org/S4210169297","display_name":"IEEE/ACM Transactions on Audio Speech and Language Processing","issn_l":"2329-9290","issn":["2329-9290","2329-9304"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","raw_type":"journal-article"},"type":"article","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/A5101607498","display_name":"Zhong-Qiu Wang","orcid":"https://orcid.org/0000-0002-4204-9430"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhong-Qiu Wang","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories, Cambridge, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-4204-9430","affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories, Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086940921","display_name":"Gordon Wichern","orcid":"https://orcid.org/0000-0002-8597-6795"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gordon Wichern","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories, Cambridge, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-8597-6795","affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories, Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076453358","display_name":"Jonathan Le Roux","orcid":"https://orcid.org/0000-0002-3451-171X"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jonathan Le Roux","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories, Cambridge, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-3451-171X","affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories, Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210159266"],"apc_list":null,"apc_paid":null,"fwci":3.2011,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.92669132,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"29","issue":null,"first_page":"3476","last_page":"3490"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","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/T10860","display_name":"Speech and Audio Processing","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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9950000047683716,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reverberation","display_name":"Reverberation","score":0.851549506187439},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7502033710479736},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.7416016459465027},{"id":"https://openalex.org/keywords/monaural","display_name":"Monaural","score":0.7265298366546631},{"id":"https://openalex.org/keywords/impulse-response","display_name":"Impulse response","score":0.5636228322982788},{"id":"https://openalex.org/keywords/linear-prediction","display_name":"Linear prediction","score":0.5620241761207581},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4822964668273926},{"id":"https://openalex.org/keywords/source-separation","display_name":"Source separation","score":0.42192861437797546},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.2772466540336609},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1729196310043335}],"concepts":[{"id":"https://openalex.org/C95851461","wikidata":"https://www.wikidata.org/wiki/Q468809","display_name":"Reverberation","level":2,"score":0.851549506187439},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7502033710479736},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7416016459465027},{"id":"https://openalex.org/C102894143","wikidata":"https://www.wikidata.org/wiki/Q1323979","display_name":"Monaural","level":2,"score":0.7265298366546631},{"id":"https://openalex.org/C72279823","wikidata":"https://www.wikidata.org/wiki/Q1139726","display_name":"Impulse response","level":2,"score":0.5636228322982788},{"id":"https://openalex.org/C131109320","wikidata":"https://www.wikidata.org/wiki/Q581012","display_name":"Linear prediction","level":2,"score":0.5620241761207581},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4822964668273926},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.42192861437797546},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.2772466540336609},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1729196310043335},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taslp.2021.3129363","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taslp.2021.3129363","pdf_url":null,"source":{"id":"https://openalex.org/S4210169297","display_name":"IEEE/ACM Transactions on Audio Speech and Language Processing","issn_l":"2329-9290","issn":["2329-9290","2329-9304"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.8299999833106995,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1482149378","https://openalex.org/W1524333225","https://openalex.org/W1555217905","https://openalex.org/W1901129140","https://openalex.org/W1973669708","https://openalex.org/W2006129368","https://openalex.org/W2023331723","https://openalex.org/W2035576074","https://openalex.org/W2045043668","https://openalex.org/W2069681747","https://openalex.org/W2127851351","https://openalex.org/W2141998673","https://openalex.org/W2156676906","https://openalex.org/W2161784349","https://openalex.org/W2161804069","https://openalex.org/W2164502538","https://openalex.org/W2221409856","https://openalex.org/W2242685705","https://openalex.org/W2291877678","https://openalex.org/W2304609584","https://openalex.org/W2568308529","https://openalex.org/W2698117193","https://openalex.org/W2734774145","https://openalex.org/W2747732471","https://openalex.org/W2763188033","https://openalex.org/W2774389566","https://openalex.org/W2791524682","https://openalex.org/W2792764867","https://openalex.org/W2884797218","https://openalex.org/W2889540509","https://openalex.org/W2899625056","https://openalex.org/W2900212944","https://openalex.org/W2939777271","https://openalex.org/W2952218014","https://openalex.org/W2962715207","https://openalex.org/W2962866211","https://openalex.org/W2962905190","https://openalex.org/W2963197115","https://openalex.org/W2963446712","https://openalex.org/W2963941778","https://openalex.org/W2964058413","https://openalex.org/W2972460025","https://openalex.org/W2972541922","https://openalex.org/W2982456909","https://openalex.org/W2998657200","https://openalex.org/W3007256793","https://openalex.org/W3015191643","https://openalex.org/W3015199127","https://openalex.org/W3015679215","https://openalex.org/W3016232124","https://openalex.org/W3026111682","https://openalex.org/W3032514799","https://openalex.org/W3082256262","https://openalex.org/W3086154751","https://openalex.org/W3096073522","https://openalex.org/W3099330747","https://openalex.org/W3111051109","https://openalex.org/W3163652268","https://openalex.org/W3171278394","https://openalex.org/W6631362777","https://openalex.org/W6749825310","https://openalex.org/W6766380642","https://openalex.org/W6769802879"],"related_works":["https://openalex.org/W2621851636","https://openalex.org/W2987432963","https://openalex.org/W2103031592","https://openalex.org/W2053167664","https://openalex.org/W2079152323","https://openalex.org/W2028091563","https://openalex.org/W1967226023","https://openalex.org/W2128388559","https://openalex.org/W178436704","https://openalex.org/W2082664169"],"abstract_inverted_index":{"A":[0],"promising":[1],"approach":[2,29],"for":[3,133,140,177],"speech":[4,38,84,186],"dereverberation":[5,85,187],"is":[6,17,30,90],"based":[7,31,82],"on":[8,32,194],"supervised":[9],"learning,":[10],"where":[11],"a":[12,54,58,64,79,102,161],"deep":[13,80],"neural":[14],"network":[15],"(DNN)":[16],"trained":[18],"to":[19,73,91,143],"predict":[20],"the":[21,44,94,98,115,149,153,168,174,195],"direct":[22],"sound":[23],"from":[24,53],"noisy-reverberant":[25,183],"speech.":[26],"This":[27],"data-driven":[28],"leveraging":[33],"prior":[34],"knowledge":[35],"of":[36,97,114],"clean":[37],"patterns,":[39],"and":[40,63,104,111,182,188,198],"seldom":[41],"explicitly":[42],"exploits":[43],"linear-filter":[45,76],"structure":[46,77],"in":[47,167,180],"reverberation,":[48],"i.e.,":[49],"that":[50,108],"reverberation":[51],"results":[52,191],"linear":[55,162],"convolution":[56],"between":[57],"room":[59],"impulse":[60],"response":[61],"(RIR)":[62],"dry":[65],"source":[66],"signal.":[67],"In":[68],"this":[69,75],"work,":[70],"we":[71,151],"propose":[72],"exploit":[74],"within":[78],"learning":[81],"monaural":[83],"framework.":[86],"The":[87],"key":[88],"idea":[89],"first":[92],"estimate":[93,152],"direct-path":[95,117],"signal":[96],"target":[99],"speaker":[100,178,189],"using":[101],"DNN":[103,142],"then":[105,172],"identify":[106,148],"signals":[107],"are":[109,192],"decayed":[110],"delayed":[112],"copies":[113],"estimated":[116],"signal,":[118],"as":[119,125,137],"these":[120],"can":[121,128],"be":[122,129],"reliably":[123],"considered":[124],"reverberation.":[126],"They":[127],"either":[130],"directly":[131],"removed":[132],"dereverberation,":[134],"or":[135],"used":[136],"extra":[138],"features":[139],"another":[141],"perform":[144],"better":[145],"dereverberation.":[146],"To":[147],"copies,":[150],"underlying":[154],"filter":[155],"(or":[156],"RIR)":[157],"by":[158],"efficiently":[159],"solving":[160],"regression":[163],"problem":[164],"per":[165],"frequency":[166],"time-frequency":[169],"domain.":[170],"We":[171],"modify":[173],"proposed":[175],"algorithm":[176],"separation":[179,190],"reverberant":[181],"conditions.":[184],"State-of-the-art":[185],"obtained":[193],"REVERB,":[196],"SMS-WSJ,":[197],"WHAMR!":[199],"datasets.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}