{"id":"https://openalex.org/W4392122255","doi":"https://doi.org/10.1109/taslp.2024.3369535","title":"Multichannel Linear Prediction-Based Speech Dereverberation Considering Sparse and Low-Rank Priors","display_name":"Multichannel Linear Prediction-Based Speech Dereverberation Considering Sparse and Low-Rank Priors","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4392122255","doi":"https://doi.org/10.1109/taslp.2024.3369535"},"language":"en","primary_location":{"id":"doi:10.1109/taslp.2024.3369535","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/taslp.2024.3369535","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/A5034486612","display_name":"Taihui Wang","orcid":"https://orcid.org/0000-0003-3324-6878"},"institutions":[{"id":"https://openalex.org/I4210099069","display_name":"Institute of Acoustics","ror":"https://ror.org/00v8rqv75","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210099069"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Taihui Wang","raw_affiliation_strings":["Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China","University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210099069","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088185643","display_name":"Feiran Yang","orcid":"https://orcid.org/0000-0002-1734-3785"},"institutions":[{"id":"https://openalex.org/I4210099069","display_name":"Institute of Acoustics","ror":"https://ror.org/00v8rqv75","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210099069"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feiran Yang","raw_affiliation_strings":["State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210099069","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100381075","display_name":"Jun Yang","orcid":"https://orcid.org/0000-0003-2807-6130"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210099069","display_name":"Institute of Acoustics","ror":"https://ror.org/00v8rqv75","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210099069"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Yang","raw_affiliation_strings":["Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China","University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210099069","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034486612"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210099069","https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.3735,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.4871232,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"32","issue":null,"first_page":"1724","last_page":"1735"},"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":0.9994999766349792,"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/T11447","display_name":"Blind Source Separation Techniques","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/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.6886794567108154},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.663284182548523},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6004599332809448},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.526424765586853},{"id":"https://openalex.org/keywords/linear-prediction","display_name":"Linear prediction","score":0.5111380219459534},{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.5016918182373047},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.48289966583251953},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.4331151247024536},{"id":"https://openalex.org/keywords/hessian-matrix","display_name":"Hessian matrix","score":0.42731598019599915},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.41203010082244873},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3501173257827759},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32682114839553833},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3154502511024475},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.2707023024559021},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23096516728401184},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17488878965377808},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.10280078649520874},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.09557300806045532}],"concepts":[{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.6886794567108154},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.663284182548523},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6004599332809448},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.526424765586853},{"id":"https://openalex.org/C131109320","wikidata":"https://www.wikidata.org/wiki/Q581012","display_name":"Linear prediction","level":2,"score":0.5111380219459534},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.5016918182373047},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.48289966583251953},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4331151247024536},{"id":"https://openalex.org/C203616005","wikidata":"https://www.wikidata.org/wiki/Q620495","display_name":"Hessian matrix","level":2,"score":0.42731598019599915},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.41203010082244873},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3501173257827759},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32682114839553833},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3154502511024475},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.2707023024559021},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23096516728401184},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17488878965377808},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.10280078649520874},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.09557300806045532},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taslp.2024.3369535","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/taslp.2024.3369535","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":[],"awards":[{"id":"https://openalex.org/G1468160670","display_name":null,"funder_award_id":"62171438","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8931139028","display_name":null,"funder_award_id":"4242013","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W173461423","https://openalex.org/W1512562331","https://openalex.org/W1546020483","https://openalex.org/W1560969610","https://openalex.org/W1635512741","https://openalex.org/W1973669708","https://openalex.org/W1983812858","https://openalex.org/W1991146156","https://openalex.org/W2006129368","https://openalex.org/W2008335297","https://openalex.org/W2036763972","https://openalex.org/W2039844283","https://openalex.org/W2093974379","https://openalex.org/W2096819401","https://openalex.org/W2097584344","https://openalex.org/W2107992675","https://openalex.org/W2110913111","https://openalex.org/W2114782757","https://openalex.org/W2117678320","https://openalex.org/W2119367741","https://openalex.org/W2131968171","https://openalex.org/W2136883355","https://openalex.org/W2142117481","https://openalex.org/W2161195301","https://openalex.org/W2164502538","https://openalex.org/W2188162373","https://openalex.org/W2242685705","https://openalex.org/W2508393166","https://openalex.org/W2534983781","https://openalex.org/W2542605056","https://openalex.org/W2587329895","https://openalex.org/W2609317876","https://openalex.org/W2791524682","https://openalex.org/W2804917410","https://openalex.org/W2898519127","https://openalex.org/W2930641205","https://openalex.org/W2962780374","https://openalex.org/W2990078986","https://openalex.org/W2998251729","https://openalex.org/W3011410532","https://openalex.org/W3016160783","https://openalex.org/W3095913803","https://openalex.org/W3104019135","https://openalex.org/W3111051109","https://openalex.org/W3111346731","https://openalex.org/W3151947803","https://openalex.org/W3162109944","https://openalex.org/W3207551191","https://openalex.org/W4210408276","https://openalex.org/W4221080854","https://openalex.org/W4251329906","https://openalex.org/W4296927107","https://openalex.org/W4372260174","https://openalex.org/W6630574771","https://openalex.org/W6680210033","https://openalex.org/W6686978693"],"related_works":["https://openalex.org/W2530685530","https://openalex.org/W4375868962","https://openalex.org/W2011227383","https://openalex.org/W2088854863","https://openalex.org/W4402568167","https://openalex.org/W2551137307","https://openalex.org/W2098101267","https://openalex.org/W2113526703","https://openalex.org/W2123043102","https://openalex.org/W2577807713"],"abstract_inverted_index":{"This":[0],"article":[1],"addresses":[2],"the":[3,14,24,31,35,42,48,61,78,92,105,110,124,127,144,156,167,176,179],"multi-channel":[4],"linear":[5],"prediction":[6,106],"(MCLP)-based":[7],"speech":[8,20,95],"dereverberation":[9,96],"problem":[10,102],"by":[11,84,109],"jointly":[12],"considering":[13],"sparsity":[15],"and":[16,34,51,114,134,150,162,186],"low-rank":[17,88,115],"priors":[18],"of":[19,70,112,143,173,178],"spectrograms.":[21],"We":[22],"utilize":[23],"complex":[25],"generalized":[26,36],"Gaussian":[27],"(CGG)":[28],"distribution":[29,63],"as":[30,41,99,140],"source":[32],"model":[33,50],"nonnegative":[37],"matrix":[38],"factorization":[39],"(NMF)":[40],"spectral":[43],"model.":[44],"The":[45,130],"difference":[46],"between":[47],"presented":[49],"existing":[52],"ones":[53],"for":[54],"MCLP":[55],"is":[56,82,97,120],"twofold.":[57],"First,":[58],"we":[59],"adopt":[60],"CGG":[62],"with":[64,72,147],"a":[65,73,87,154,160],"time-frequency-variant":[66],"scale":[67,75,80],"parameter":[68,81],"instead":[69],"that":[71,103],"time-frequency-invariant":[74],"parameter.":[76],"Second,":[77],"time-frequency-varying":[79],"approximated":[83],"NMF":[85],"in":[86],"manner.":[89],"Based":[90],"on":[91],"maximum-likelihood":[93],"criterion,":[94],"formulated":[98],"an":[100],"optimization":[101],"minimizes":[104],"error":[107],"weighted":[108],"reciprocal":[111],"sparse":[113],"parameters.":[116,152],"A":[117,171],"convergence-guaranteed":[118],"algorithm":[119],"derived":[121],"to":[122,165],"estimate":[123],"parameters":[125],"using":[126],"majorization-minimization":[128],"technology.":[129],"WPE,":[131,183],"NMF-based":[132,184],"WPE":[133,136,169,185,188],"CGG-based":[135,168,187],"can":[137],"be":[138],"treated":[139],"special":[141],"cases":[142],"proposed":[145,157,180],"method":[146,158,181],"different":[148],"shape":[149],"domain":[151],"As":[153],"byproduct,":[155],"provides":[159],"simple":[161],"elegant":[163],"way":[164],"derive":[166],"algorithm.":[170],"series":[172],"experiments":[174],"show":[175],"superiority":[177],"over":[182],"methods.":[189]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}