{"id":"https://openalex.org/W4408725466","doi":"https://doi.org/10.1186/s13634-025-01210-1","title":"TFDense-GAN: a generative adversarial network for single-channel speech enhancement","display_name":"TFDense-GAN: a generative adversarial network for single-channel speech enhancement","publication_year":2025,"publication_date":"2025-03-22","ids":{"openalex":"https://openalex.org/W4408725466","doi":"https://doi.org/10.1186/s13634-025-01210-1"},"language":"en","primary_location":{"id":"doi:10.1186/s13634-025-01210-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-025-01210-1","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-025-01210-1","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-025-01210-1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101472579","display_name":"Haoxiang Chen","orcid":"https://orcid.org/0000-0002-5697-1442"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoxiang Chen","raw_affiliation_strings":["Beijing Forestry University, 35 Qinghua East Road, Haidian District, Beijing, 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Forestry University, 35 Qinghua East Road, Haidian District, Beijing, 100083, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118983721","display_name":"Jinxiu Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinxiu Zhang","raw_affiliation_strings":["Beijing Forestry University, 35 Qinghua East Road, Haidian District, Beijing, 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Forestry University, 35 Qinghua East Road, Haidian District, Beijing, 100083, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065339186","display_name":"Yanjun Fu","orcid":"https://orcid.org/0000-0002-8835-6014"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaogang Fu","raw_affiliation_strings":["Beijing Forestry University, 35 Qinghua East Road, Haidian District, Beijing, 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Forestry University, 35 Qinghua East Road, Haidian District, Beijing, 100083, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070005614","display_name":"Xintong Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xintong Zhou","raw_affiliation_strings":["Beijing Forestry University, 35 Qinghua East Road, Haidian District, Beijing, 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Forestry University, 35 Qinghua East Road, Haidian District, Beijing, 100083, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119847881","display_name":"Ruilong Wang","orcid":"https://orcid.org/0009-0002-9369-0284"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruilong Wang","raw_affiliation_strings":["Beijing Forestry University, 35 Qinghua East Road, Haidian District, Beijing, 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Forestry University, 35 Qinghua East Road, Haidian District, Beijing, 100083, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100704964","display_name":"Yanyan Xu","orcid":"https://orcid.org/0000-0001-7174-6588"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanyan Xu","raw_affiliation_strings":["Beijing Forestry University, 35 Qinghua East Road, Haidian District, Beijing, 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Forestry University, 35 Qinghua East Road, Haidian District, Beijing, 100083, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102845007","display_name":"Dengfeng Ke","orcid":"https://orcid.org/0000-0001-8459-0412"},"institutions":[{"id":"https://openalex.org/I115212828","display_name":"Beijing Language and Culture University","ror":"https://ror.org/03te2zs36","country_code":"CN","type":"education","lineage":["https://openalex.org/I115212828"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dengfeng Ke","raw_affiliation_strings":["Beijing Language and Culture University, 15 Xueyuan Road, Haidian District, Beijing, 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Language and Culture University, 15 Xueyuan Road, Haidian District, Beijing, 100083, China","institution_ids":["https://openalex.org/I115212828"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1140,"currency":"GBP","value_usd":1398},"apc_paid":{"value":1140,"currency":"GBP","value_usd":1398},"fwci":1.8537,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.83366636,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"2025","issue":"1","first_page":null,"last_page":null},"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/T11309","display_name":"Music and Audio Processing","score":0.9965999722480774,"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":0.994700014591217,"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/adversarial-system","display_name":"Adversarial system","score":0.7260000705718994},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6942514181137085},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5715947151184082},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.5710972547531128},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5167936086654663},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.42419931292533875},{"id":"https://openalex.org/keywords/speech-enhancement","display_name":"Speech enhancement","score":0.4179134964942932},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.32923123240470886},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2978720963001251},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.14827358722686768},{"id":"https://openalex.org/keywords/background-noise","display_name":"Background noise","score":0.07703089714050293}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7260000705718994},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6942514181137085},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5715947151184082},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.5710972547531128},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5167936086654663},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.42419931292533875},{"id":"https://openalex.org/C2776182073","wikidata":"https://www.wikidata.org/wiki/Q7575395","display_name":"Speech enhancement","level":3,"score":0.4179134964942932},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.32923123240470886},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2978720963001251},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.14827358722686768},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.07703089714050293}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s13634-025-01210-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-025-01210-1","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-025-01210-1","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:d25ab9325b92464ebc4b53e2e8ad4cd3","is_oa":true,"landing_page_url":"https://doaj.org/article/d25ab9325b92464ebc4b53e2e8ad4cd3","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EURASIP Journal on Advances in Signal Processing, Vol 2025, Iss 1, Pp 1-24 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s13634-025-01210-1","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-025-01210-1","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-025-01210-1","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4408725466.pdf"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1552314771","https://openalex.org/W1983108229","https://openalex.org/W2094721231","https://openalex.org/W2141998673","https://openalex.org/W2144404214","https://openalex.org/W2943554574","https://openalex.org/W2952218014","https://openalex.org/W2963103134","https://openalex.org/W2964058413","https://openalex.org/W2998161426","https://openalex.org/W3016129867","https://openalex.org/W3095057960","https://openalex.org/W3096408984","https://openalex.org/W3096893582","https://openalex.org/W3097034112","https://openalex.org/W3097906045","https://openalex.org/W3097945073","https://openalex.org/W3099330747","https://openalex.org/W3158779859","https://openalex.org/W3160085755","https://openalex.org/W3161950572","https://openalex.org/W3162188526","https://openalex.org/W3162493033","https://openalex.org/W3197729725","https://openalex.org/W3206809722","https://openalex.org/W3213188934","https://openalex.org/W4221162870","https://openalex.org/W4224917453","https://openalex.org/W4280579494","https://openalex.org/W4283326514","https://openalex.org/W4304141208","https://openalex.org/W4312095910","https://openalex.org/W4385822313","https://openalex.org/W4385823093","https://openalex.org/W4399145914","https://openalex.org/W6600669965"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W2888032422","https://openalex.org/W2996316059","https://openalex.org/W4377980832","https://openalex.org/W2897769091","https://openalex.org/W2845413374","https://openalex.org/W3005996785","https://openalex.org/W4297411772","https://openalex.org/W4235873501"],"abstract_inverted_index":{"Research":[0],"indicates":[1],"that":[2,197,256],"utilizing":[3],"the":[4,7,30,35,38,44,54,62,64,67,73,76,94,97,123,126,134,155,167,172,176,188,193,210,219,240,246],"spectrum":[5],"in":[6,14,34,71,90,121,133,154,214,218,262],"time\u2013frequency":[8,36,107],"domain":[9,108],"plays":[10],"a":[11,105,180,207,215],"crucial":[12],"role":[13],"speech":[15,31,101,148],"enhancement":[16,32,149],"tasks,":[17,150],"as":[18,206],"it":[19],"can":[20,191,280],"better":[21],"extract":[22],"audio":[23],"features":[24],"and":[25,43,66,75,125,128,139,170,222,234,245,267,277],"reduce":[26],"computational":[27],"consumption.":[28],"For":[29],"methods":[33,99],"domain,":[37],"introduction":[39],"of":[40,46,96,175,264,275],"attention":[41,131],"mechanisms":[42],"application":[45],"DenseBlock":[47,70,117],"have":[48],"yielded":[49],"promising":[50],"results.":[51,271],"In":[52,87],"particular,":[53],"Unet":[55],"architecture,":[56],"which":[57],"comprises":[58],"three":[59],"main":[60],"components,":[61],"encoder,":[63],"decoder,":[65],"bottleneck,":[68],"employs":[69,129],"both":[72,122],"encoder":[74,124],"decoder":[77,127],"to":[78,92,159,164],"achieve":[79],"powerful":[80],"feature":[81,119,137],"fusion":[82,138],"capabilities":[83],"with":[84],"fewer":[85],"parameters.":[86],"this":[88,225],"paper,":[89],"order":[91],"enhance":[93,166],"advantages":[95],"aforementioned":[98],"for":[100,118,136,147],"enhancement,":[102],"we":[103,178,203,223],"propose":[104],"Unet-based":[106],"denoising":[109,168,220],"model":[110,142,227],"called":[111],"TFDense-Net.":[112],"It":[113],"utilizes":[114],"our":[115,231],"improved":[116],"extraction":[120],"an":[130],"mechanism":[132],"bottleneck":[135],"denoising.":[140],"The":[141,272],"has":[143],"demonstrated":[144],"excellent":[145],"performance":[146,169],"achieving":[151,269],"significant":[152,216],"improvements":[153],"Si-SDR":[156],"metric":[157],"compared":[158],"other":[160,278],"state-of-the-art":[161,270],"models.":[162],"Additionally,":[163],"further":[165],"increase":[171],"receptive":[173],"field":[174],"model,":[177],"introduce":[179],"multi-spectrogram":[181,211],"discriminator":[182,189],"based":[183],"on":[184,236],"multiple":[185],"STFTs.":[186],"Since":[187],"loss":[190,199],"observe":[192],"correlations":[194],"between":[195],"spectra":[196],"traditional":[198],"functions":[200],"cannot":[201],"detect,":[202],"train":[204],"TFDense-Net":[205,233],"generator":[208],"against":[209],"discriminator,":[212],"resulting":[213],"improvement":[217],"performance,":[221],"name":[224],"enhanced":[226],"TFDense-GAN.":[228],"We":[229],"evaluate":[230],"proposed":[232],"TFDense-GAN":[235,257,276],"two":[237],"public":[238],"datasets:":[239],"VCTK":[241],"+":[242],"DEMAND":[243],"dataset":[244],"Interspeech":[247],"Deep":[248],"Noise":[249],"Suppression":[250],"Challenge":[251],"dataset.":[252],"Experimental":[253],"results":[254],"show":[255],"outperforms":[258],"most":[259],"existing":[260],"models":[261,279],"terms":[263],"STOI,":[265],"PESQ,":[266],"Si-SDR,":[268],"comparison":[273],"samples":[274],"be":[281],"accessed":[282],"from":[283],"https://github.com/yhsjoker/TFDense-GAN":[284],".":[285]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}
