{"id":"https://openalex.org/W3030191531","doi":"https://doi.org/10.21437/interspeech.2020-3115","title":"Unsupervised Audio Source Separation Using Generative Priors","display_name":"Unsupervised Audio Source Separation Using Generative Priors","publication_year":2020,"publication_date":"2020-10-25","ids":{"openalex":"https://openalex.org/W3030191531","doi":"https://doi.org/10.21437/interspeech.2020-3115","mag":"3030191531"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2020-3115","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2020-3115","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2020","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2005.13769","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090107273","display_name":"Vivek Narayanaswamy","orcid":"https://orcid.org/0000-0002-6570-2930"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vivek Narayanaswamy","raw_affiliation_strings":["Arizona State University**"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University**","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046632395","display_name":"Jayaraman J. Thiagarajan","orcid":"https://orcid.org/0000-0002-8517-5816"},"institutions":[{"id":"https://openalex.org/I1282311441","display_name":"Lawrence Livermore National Laboratory","ror":"https://ror.org/041nk4h53","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282311441","https://openalex.org/I1330989302","https://openalex.org/I198811213","https://openalex.org/I4210138311"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jayaraman J. Thiagarajan","raw_affiliation_strings":["Lawrence livermore National Laboratory"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lawrence livermore National Laboratory","institution_ids":["https://openalex.org/I1282311441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026742436","display_name":"Rushil Anirudh","orcid":"https://orcid.org/0000-0002-4186-3502"},"institutions":[{"id":"https://openalex.org/I1282311441","display_name":"Lawrence Livermore National Laboratory","ror":"https://ror.org/041nk4h53","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282311441","https://openalex.org/I1330989302","https://openalex.org/I198811213","https://openalex.org/I4210138311"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rushil Anirudh","raw_affiliation_strings":["Lawrence livermore National Laboratory"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lawrence livermore National Laboratory","institution_ids":["https://openalex.org/I1282311441"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074371899","display_name":"Andreas Spanias","orcid":"https://orcid.org/0000-0003-0306-9348"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andreas Spanias","raw_affiliation_strings":["Arizona State University**"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University**","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9124,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.7372815,"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":"2657","last_page":"2661"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9995999932289124,"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/T10822","display_name":"Acoustic Wave Phenomena Research","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/prior-probability","display_name":"Prior probability","score":0.7958967685699463},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7517958879470825},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6818755269050598},{"id":"https://openalex.org/keywords/source-separation","display_name":"Source separation","score":0.5749066472053528},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5375015735626221},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4688728153705597},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4625633656978607},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.44101792573928833},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43679141998291016},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4195197522640228},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3625505864620209},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11260724067687988},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.10221582651138306}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7958967685699463},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7517958879470825},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6818755269050598},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.5749066472053528},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5375015735626221},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4688728153705597},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4625633656978607},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.44101792573928833},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43679141998291016},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4195197522640228},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3625505864620209},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11260724067687988},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.10221582651138306},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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":4,"locations":[{"id":"doi:10.21437/interspeech.2020-3115","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2020-3115","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2020","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2005.13769","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2005.13769","pdf_url":"https://arxiv.org/pdf/2005.13769","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":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":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:3030191531","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2005.13769.pdf","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_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2005.13769","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2005.13769","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_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2005.13769","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2005.13769","pdf_url":"https://arxiv.org/pdf/2005.13769","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":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":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3030191531.pdf","grobid_xml":"https://content.openalex.org/works/W3030191531.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W186036538","https://openalex.org/W1497843931","https://openalex.org/W1901129140","https://openalex.org/W1985834699","https://openalex.org/W2079985975","https://openalex.org/W2099471712","https://openalex.org/W2101234009","https://openalex.org/W2105732805","https://openalex.org/W2150415460","https://openalex.org/W2167752533","https://openalex.org/W2173520492","https://openalex.org/W2240558130","https://openalex.org/W2395936047","https://openalex.org/W2595294663","https://openalex.org/W2783473931","https://openalex.org/W2787898208","https://openalex.org/W2797583228","https://openalex.org/W2798966709","https://openalex.org/W2890043615","https://openalex.org/W2890855852","https://openalex.org/W2898606530","https://openalex.org/W2950299304","https://openalex.org/W2952218014","https://openalex.org/W2962793481","https://openalex.org/W2962879692","https://openalex.org/W2963452667","https://openalex.org/W2963519193","https://openalex.org/W2963751183","https://openalex.org/W2963992487","https://openalex.org/W2964013315","https://openalex.org/W2971458685","https://openalex.org/W2996158613","https://openalex.org/W3009309344"],"related_works":["https://openalex.org/W3095999710","https://openalex.org/W2770809586","https://openalex.org/W3034218934","https://openalex.org/W3111308920","https://openalex.org/W2794935216","https://openalex.org/W3120575095","https://openalex.org/W2551835155","https://openalex.org/W3192693288","https://openalex.org/W3007111626","https://openalex.org/W3118272953","https://openalex.org/W3206212295","https://openalex.org/W2900553892","https://openalex.org/W2995541282","https://openalex.org/W69112019","https://openalex.org/W2153939756","https://openalex.org/W2966183206","https://openalex.org/W3176254306","https://openalex.org/W3199732964","https://openalex.org/W2949647590","https://openalex.org/W3118307428"],"abstract_inverted_index":{"State-of-the-art":[0],"under-determined":[1],"audio":[2,101],"source":[3,39,102,163],"separation":[4,103],"systems":[5],"rely":[6],"on":[7,105,109,168],"supervised":[8],"end-end":[9],"training":[10],"of":[11,34,50,86,115,179],"carefully":[12],"tailored":[13],"neural":[14],"network":[15],"architectures":[16],"operating":[17],"either":[18],"in":[19,32,78,124,142],"the":[20,23,53,67,75,84,113,125,132,136,143,177],"time":[21,144],"or":[22],"spectral":[24,153],"domain.":[25],"However,":[26],"these":[27],"methods":[28,71],"are":[29],"severely":[30],"challenged":[31],"terms":[33],"requiring":[35],"access":[36],"to":[37,46,129,161],"expensive":[38],"level":[40],"labeled":[41,87],"data":[42,88],"and":[43,52,81,172],"being":[44],"specific":[45],"a":[47,97],"given":[48],"set":[49],"sources":[51],"mixing":[54],"process,":[55],"which":[56],"demands":[57],"complete":[58],"re-training":[59],"when":[60],"those":[61],"assumptions":[62],"change.":[63],"This":[64],"strongly":[65],"emphasizes":[66],"need":[68],"for":[69,83,100,157],"unsupervised":[70,188],"that":[72,151],"can":[73,139],"leverage":[74],"recent":[76],"advances":[77],"data-driven":[79],"modeling,":[80],"compensate":[82],"lack":[85],"through":[89],"meaningful":[90],"priors.":[91],"To":[92],"this":[93],"end,":[94],"we":[95,149],"propose":[96],"novel":[98],"approach":[99,121,181],"based":[104],"generative":[106,137],"priors":[107,138],"trained":[108],"individual":[110],"sources.":[111,134],"Through":[112],"use":[114],"projected":[116],"gradient":[117],"descent":[118],"optimization,":[119],"our":[120,158,180],"simultaneously":[122],"searches":[123],"source-specific":[126],"latent":[127],"spaces":[128],"effectively":[130],"recover":[131],"constituent":[133],"Though":[135],"be":[140],"defined":[141],"domain":[145,154],"directly,":[146],"e.g.":[147],"WaveGAN,":[148],"find":[150],"using":[152],"loss":[155],"functions":[156],"optimization":[159],"leads":[160],"good-quality":[162],"estimates.":[164],"Our":[165],"empirical":[166],"studies":[167],"standard":[169],"spoken":[170],"digit":[171],"instrument":[173],"datasets":[174],"clearly":[175],"demonstrate":[176],"effectiveness":[178],"over":[182],"classical":[183],"as":[184,186],"well":[185],"state-of-the-art":[187],"baselines.":[189]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}