{"id":"https://openalex.org/W3011129471","doi":"https://doi.org/10.1109/apsipaasc47483.2019.9023013","title":"Single Channel Speech Enhancement Using Temporal Convolutional Recurrent Neural Networks","display_name":"Single Channel Speech Enhancement Using Temporal Convolutional Recurrent Neural Networks","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3011129471","doi":"https://doi.org/10.1109/apsipaasc47483.2019.9023013","mag":"3011129471"},"language":"en","primary_location":{"id":"doi:10.1109/apsipaasc47483.2019.9023013","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc47483.2019.9023013","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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/A5100642253","display_name":"Jingdong Li","orcid":"https://orcid.org/0000-0003-2335-6280"},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingdong Li","raw_affiliation_strings":["College of Computer Science, Inner Mongolian University, Hohhot, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Inner Mongolian University, Hohhot, China","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100715414","display_name":"Hui Zhang","orcid":"https://orcid.org/0000-0001-8012-4684"},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Zhang","raw_affiliation_strings":["College of Computer Science, Inner Mongolian University, Hohhot, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Inner Mongolian University, Hohhot, China","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100693230","display_name":"Xueliang Zhang","orcid":"https://orcid.org/0000-0002-0406-1105"},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueliang Zhang","raw_affiliation_strings":["College of Computer Science, Inner Mongolian University, Hohhot, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Inner Mongolian University, Hohhot, China","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101857422","display_name":"Changliang Li","orcid":"https://orcid.org/0000-0003-2236-9266"},"institutions":[{"id":"https://openalex.org/I4210108461","display_name":"Kingsoft (China)","ror":"https://ror.org/01stnfn33","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210108461"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changliang Li","raw_affiliation_strings":["Kingsoft AI Laboratory, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Kingsoft AI Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210108461"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100642253"],"corresponding_institution_ids":["https://openalex.org/I2722730"],"apc_list":null,"apc_paid":null,"fwci":0.9951,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.7763594,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"896","last_page":"900"},"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":0.9979000091552734,"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":0.9973000288009644,"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/computer-science","display_name":"Computer science","score":0.7318496704101562},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6624583005905151},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6087614297866821},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5051329731941223},{"id":"https://openalex.org/keywords/speech-enhancement","display_name":"Speech enhancement","score":0.48990529775619507},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.44350650906562805},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3732183575630188},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.23414626717567444},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.13018041849136353}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7318496704101562},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6624583005905151},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6087614297866821},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5051329731941223},{"id":"https://openalex.org/C2776182073","wikidata":"https://www.wikidata.org/wiki/Q7575395","display_name":"Speech enhancement","level":3,"score":0.48990529775619507},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.44350650906562805},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3732183575630188},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.23414626717567444},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.13018041849136353},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipaasc47483.2019.9023013","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc47483.2019.9023013","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5199999809265137,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1482149378","https://openalex.org/W1522301498","https://openalex.org/W1542280630","https://openalex.org/W1552314771","https://openalex.org/W1836465849","https://openalex.org/W1974387177","https://openalex.org/W2013608223","https://openalex.org/W2044893557","https://openalex.org/W2069681747","https://openalex.org/W2078528584","https://openalex.org/W2086139506","https://openalex.org/W2120847449","https://openalex.org/W2128653836","https://openalex.org/W2153894152","https://openalex.org/W2168379380","https://openalex.org/W2291877678","https://openalex.org/W2398826216","https://openalex.org/W2519091744","https://openalex.org/W2559260703","https://openalex.org/W2678916739","https://openalex.org/W2888169323","https://openalex.org/W2889286744","https://openalex.org/W2889442120","https://openalex.org/W2962866211","https://openalex.org/W2963071736","https://openalex.org/W2963103134","https://openalex.org/W2963828919","https://openalex.org/W3103913581","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6712560600"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W3032952384","https://openalex.org/W2578422401","https://openalex.org/W2770665941","https://openalex.org/W3096184950","https://openalex.org/W4231424160","https://openalex.org/W2275432853","https://openalex.org/W197907117"],"abstract_inverted_index":{"In":[0,36],"recent":[1],"decades,":[2],"neural":[3,66],"network":[4,45],"based":[5],"methods":[6],"have":[7],"significantly":[8],"improved":[9],"the":[10,32,41,82,102,120],"performance":[11,103],"of":[12,16,22,104,138],"speech":[13,24,89,133,139],"enhancement.":[14],"Most":[15],"them":[17],"estimate":[18],"time-frequency":[19],"(T-F)":[20],"representation":[21],"target":[23],"directly":[25,51],"or":[26],"indirectly,":[27],"then":[28],"resynthesize":[29],"waveform":[30,54],"using":[31],"estimated":[33],"T-F":[34],"representation.":[35],"this":[37],"work,":[38],"we":[39,80],"proposed":[40],"temporal":[42],"convolutional":[43,109],"recurrent":[44,65,110],"(TCRN),":[46],"an":[47],"end-to-end":[48],"model":[49,97,129],"that":[50,84,95,127],"map":[52],"noisy":[53],"to":[55,70,100,118],"clean":[56],"waveform.":[57],"The":[58,123],"TCRN,":[59],"which":[60],"is":[61,68,98],"combined":[62],"convolution":[63],"and":[64,72,87,141],"network,":[67],"able":[69,99],"efficiently":[71],"effectively":[73],"leverage":[74],"short-term":[75],"ang":[76],"long-term":[77],"information.":[78],"Furthermore,":[79,112],"present":[81,114],"architecture":[83],"iterately":[85],"downsample":[86],"upsample":[88],"during":[90],"forward":[91],"propagation.":[92],"We":[93,113],"show":[94,126],"our":[96,128],"improve":[101],"model,":[105],"compared":[106],"with":[107],"existing":[108,132],"networks.":[111],"several":[115],"key":[116],"techniques":[117],"stabilize":[119],"training":[121],"process.":[122],"experimental":[124],"results":[125],"consistently":[130],"outperforms":[131],"enhancement":[134],"approaches,":[135],"in":[136],"terms":[137],"intelligibility":[140],"quality.":[142]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
