{"id":"https://openalex.org/W4310258506","doi":"https://doi.org/10.48550/arxiv.2206.07293","title":"FRCRN: Boosting Feature Representation using Frequency Recurrence for Monaural Speech Enhancement","display_name":"FRCRN: Boosting Feature Representation using Frequency Recurrence for Monaural Speech Enhancement","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4310258506","doi":"https://doi.org/10.48550/arxiv.2206.07293"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2206.07293","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2206.07293","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112138727","display_name":"Shengkui Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Shengkui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100749734","display_name":"Bin Ma","orcid":"https://orcid.org/0000-0002-9030-7393"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Bin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076817923","display_name":"Karn N. Watcharasupat","orcid":"https://orcid.org/0000-0002-3878-5048"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Watcharasupat, Karn N.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5072584895","display_name":"Woon\u2010Seng Gan","orcid":"https://orcid.org/0000-0002-7143-1823"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gan, Woon-Seng","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.673644,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":59,"max":69},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9993,"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":0.9993,"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":0.9975,"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/T10403","display_name":"Phonetics and Phonology Research","score":0.9656,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5993294},{"id":"https://openalex.org/keywords/wideband","display_name":"Wideband","score":0.49641663},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature Learning","score":0.48051724},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.42959365}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.73561716},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5993294},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.5724896},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.56900924},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.56713676},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.51508373},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.50817966},{"id":"https://openalex.org/C2780202535","wikidata":"https://www.wikidata.org/wiki/Q4524457","display_name":"Wideband","level":2,"score":0.49641663},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.48051724},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44137764},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.42959365},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42060518},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.18192229},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.115187794},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.11241686},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0769625},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2206.07293","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.07293","pdf_url":"http://arxiv.org/pdf/2206.07293","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2206.07293","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2206.07293","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5,"display_name":"Peace, justice, and strong institutions"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4390516098","https://openalex.org/W4384026382","https://openalex.org/W3112081258","https://openalex.org/W2792514479","https://openalex.org/W2392214114","https://openalex.org/W2332949289","https://openalex.org/W2315892906","https://openalex.org/W1978532702","https://openalex.org/W169484559","https://openalex.org/W1480343695"],"abstract_inverted_index":{"Convolutional":[0],"recurrent":[1,12,48],"networks":[2],"(CRN)":[3],"integrating":[4],"a":[5,11,46,101],"convolutional":[6,47,67],"encoder-decoder":[7,49],"(CED)":[8],"structure":[9,13,51],"and":[10,86,119,150,156,169,185],"have":[14],"achieved":[15,162,170],"promising":[16],"performance":[17,164],"for":[18,173],"monaural":[19],"speech":[20,91],"enhancement.":[21],"However,":[22],"feature":[23,54,68,88],"representation":[24,55],"across":[25],"frequency":[26,58,63,72,84,95],"context":[27],"is":[28,79,97],"highly":[29],"constrained":[30],"due":[31],"to":[32,52,122,140],"limited":[33],"receptive":[34],"fields":[35],"in":[36,147,178,189],"the":[37,57,71,108,117,120,129,174,190],"convolutions":[38],"of":[39,81,90,180],"CED.":[40],"In":[41],"this":[42],"paper,":[43],"we":[44,110],"propose":[45],"(CRED)":[50],"boost":[53],"along":[56,70],"axis.":[59],"The":[60,93],"CRED":[61],"applies":[62],"recurrence":[64,96],"on":[65,165],"3D":[66],"maps":[69],"axis":[73],"following":[74],"each":[75],"convolution,":[76],"therefore,":[77],"it":[78],"capable":[80],"catching":[82],"long-range":[83],"correlations":[85],"enhancing":[87],"representations":[89],"inputs.":[92],"proposed":[94,130,160],"realized":[98],"efficiently":[99],"using":[100,153],"feedforward":[102],"sequential":[103],"memory":[104],"network":[105],"(FSMN).":[106],"Besides":[107],"CRED,":[109],"insert":[111],"two":[112],"stacked":[113],"FSMN":[114],"layers":[115],"between":[116],"encoder":[118],"decoder":[121],"model":[123],"further":[124],"temporal":[125],"dynamics.":[126],"We":[127,137],"name":[128],"framework":[131],"as":[132],"Frequency":[133],"Recurrent":[134],"CRN":[135],"(FRCRN).":[136],"design":[138],"FRCRN":[139,152],"predict":[141],"complex":[142],"Ideal":[143],"Ratio":[144],"Mask":[145],"(cIRM)":[146],"complex-valued":[148],"domain":[149],"optimize":[151],"both":[154],"time-frequency-domain":[155],"time-domain":[157],"losses.":[158],"Our":[159],"approach":[161],"state-of-the-art":[163],"wideband":[166],"benchmark":[167],"datasets":[168],"2nd":[171],"place":[172],"real-time":[175],"fullband":[176],"track":[177],"terms":[179],"Mean":[181],"Opinion":[182],"Score":[183],"(MOS)":[184],"Word":[186],"Accuracy":[187],"(WAcc)":[188],"ICASSP":[191],"2022":[192],"Deep":[193],"Noise":[194],"Suppression":[195],"(DNS)":[196],"challenge":[197],"(https://github.com/alibabasglab/FRCRN).":[198]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4310258506","counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-04-24T03:45:24.278154","created_date":"2022-11-30"}