{"id":"https://openalex.org/W4392910819","doi":"https://doi.org/10.1109/icassp48485.2024.10447369","title":"Opine: Leveraging a Optimization-Inspired Deep Unfolding Method for Multi-Channel Speech Enhancement","display_name":"Opine: Leveraging a Optimization-Inspired Deep Unfolding Method for Multi-Channel Speech Enhancement","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392910819","doi":"https://doi.org/10.1109/icassp48485.2024.10447369"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10447369","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icassp48485.2024.10447369","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-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/A5053757616","display_name":"Andong Li","orcid":"https://orcid.org/0000-0003-4094-8448"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Andong Li","raw_affiliation_strings":["Tencent AI Lab,China","Tencent AI Lab, China"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab,China","institution_ids":["https://openalex.org/I2250653659"]},{"raw_affiliation_string":"Tencent AI Lab, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049346285","display_name":"Rilin Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rilin Chen","raw_affiliation_strings":["Tencent AI Lab,China","Tencent AI Lab, China"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab,China","institution_ids":["https://openalex.org/I2250653659"]},{"raw_affiliation_string":"Tencent AI Lab, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100352646","display_name":"Yu Gu","orcid":"https://orcid.org/0000-0001-6939-0850"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Gu","raw_affiliation_strings":["Tencent AI Lab,China","Tencent AI Lab, China"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab,China","institution_ids":["https://openalex.org/I2250653659"]},{"raw_affiliation_string":"Tencent AI Lab, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106404246","display_name":"Chao Weng","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Weng","raw_affiliation_strings":["Tencent AI Lab,China","Tencent AI Lab, China"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab,China","institution_ids":["https://openalex.org/I2250653659"]},{"raw_affiliation_string":"Tencent AI Lab, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075183307","display_name":"Dan Su","orcid":"https://orcid.org/0000-0001-5746-9545"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dan Su","raw_affiliation_strings":["Tencent AI Lab,China","Tencent AI Lab, China"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab,China","institution_ids":["https://openalex.org/I2250653659"]},{"raw_affiliation_string":"Tencent AI Lab, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5053757616"],"corresponding_institution_ids":["https://openalex.org/I2250653659"],"apc_list":null,"apc_paid":null,"fwci":0.375,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.49721598,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"11376","last_page":"11380"},"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":0.9993000030517578,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.724202573299408},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.7025172710418701},{"id":"https://openalex.org/keywords/beamforming","display_name":"Beamforming","score":0.6128976345062256},{"id":"https://openalex.org/keywords/speech-enhancement","display_name":"Speech enhancement","score":0.606319785118103},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.6029393076896667},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.5532561540603638},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5133118033409119},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.48364031314849854},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.4681938588619232},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4408136308193207},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.43239012360572815},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.42025405168533325},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41777247190475464},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4108218550682068},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4100951552391052},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2107428014278412},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.1721491515636444},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15870621800422668},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.12380534410476685},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0991518497467041}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.724202573299408},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.7025172710418701},{"id":"https://openalex.org/C54197355","wikidata":"https://www.wikidata.org/wiki/Q5782992","display_name":"Beamforming","level":2,"score":0.6128976345062256},{"id":"https://openalex.org/C2776182073","wikidata":"https://www.wikidata.org/wiki/Q7575395","display_name":"Speech enhancement","level":3,"score":0.606319785118103},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.6029393076896667},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.5532561540603638},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5133118033409119},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.48364031314849854},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.4681938588619232},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4408136308193207},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.43239012360572815},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.42025405168533325},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41777247190475464},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4108218550682068},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4100951552391052},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2107428014278412},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.1721491515636444},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15870621800422668},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.12380534410476685},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0991518497467041},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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/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},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10447369","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icassp48485.2024.10447369","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1494198834","https://openalex.org/W1552314771","https://openalex.org/W1970018012","https://openalex.org/W2038497950","https://openalex.org/W2066218102","https://openalex.org/W2100556411","https://openalex.org/W2104266187","https://openalex.org/W2152491108","https://openalex.org/W2219249508","https://openalex.org/W2289480995","https://openalex.org/W2398972335","https://openalex.org/W2400339399","https://openalex.org/W2516001803","https://openalex.org/W2517616541","https://openalex.org/W2568308529","https://openalex.org/W2784187484","https://openalex.org/W2798559986","https://openalex.org/W2803583024","https://openalex.org/W2890553422","https://openalex.org/W2964058413","https://openalex.org/W3015372568","https://openalex.org/W3015761657","https://openalex.org/W3032514799","https://openalex.org/W3048794210","https://openalex.org/W3151596526","https://openalex.org/W3162341667","https://openalex.org/W3162493033","https://openalex.org/W3164815015","https://openalex.org/W3192281612","https://openalex.org/W3196940724","https://openalex.org/W4221164004","https://openalex.org/W4294068605","https://openalex.org/W4297841326","https://openalex.org/W4310922356","https://openalex.org/W4312219134","https://openalex.org/W4312806213","https://openalex.org/W4322765508","https://openalex.org/W4372259947","https://openalex.org/W4372259962","https://openalex.org/W4375868822","https://openalex.org/W4385756463","https://openalex.org/W4385823210","https://openalex.org/W6688816777","https://openalex.org/W6847700378"],"related_works":["https://openalex.org/W2158224665","https://openalex.org/W2379589510","https://openalex.org/W2810730439","https://openalex.org/W4300044672","https://openalex.org/W1881631164","https://openalex.org/W2358292267","https://openalex.org/W3106461837","https://openalex.org/W2378166785","https://openalex.org/W1964277756","https://openalex.org/W2360887461"],"abstract_inverted_index":{"Proximal":[0],"gradient":[1,69],"theory":[2],"has":[3],"demonstrated":[4],"its":[5],"superiority":[6],"in":[7,20],"the":[8,21,46,56,67,72,79,91,101,113,122],"compressive":[9],"sensing":[10],"field":[11],"for":[12,39],"complex":[13],"signal":[14],"recovery.":[15],"As":[16],"an":[17,28],"early":[18],"trial":[19],"speech":[22,41,54],"front-end":[23],"field,":[24],"we":[25,44,87],"propose":[26,88],"OPINE,":[27],"optimization-inspired":[29],"deep":[30],"unfolding":[31],"framework":[32],"to":[33,89],"simulate":[34],"traditional":[35],"iterative":[36],"optimization":[37,48],"process":[38],"multi-channel":[40],"enhancement.":[42],"Specifically,":[43],"formulate":[45,90],"joint":[47],"of":[49,83],"beamforming":[50],"weights":[51],"and":[52,65,117],"target":[53,81],"using":[55],"Bayesian":[57],"maximum":[58],"a":[59,95],"posteriori":[60],"(MAP)":[61],"criterion.":[62],"By":[63],"splitting":[64],"introducing":[66],"proximal":[68,92],"descent":[70],"method,":[71],"original":[73],"problem":[74],"can":[75,125],"be":[76],"formulated":[77],"into":[78,94],"alternating":[80],"solving":[82],"two":[84],"sub-problems.":[85],"Furthermore,":[86],"function":[93],"more":[96],"generalized":[97],"NN-based":[98],"modules,":[99],"enabling":[100],"end-to-end":[102],"learning":[103],"from":[104],"massive":[105],"training":[106],"data.":[107],"The":[108],"experiments":[109],"are":[110],"conducted":[111],"on":[112],"spatialized":[114],"LibriSpeech":[115],"dataset,":[116],"quantitative":[118],"results":[119],"show":[120],"that":[121],"proposed":[123],"method":[124],"achieve":[126],"comparable":[127],"performance":[128],"over":[129],"existing":[130],"advanced":[131],"baselines.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}