{"id":"https://openalex.org/W2899647550","doi":"https://doi.org/10.1109/icassp.2019.8683198","title":"Bootstrapping Single-channel Source Separation via Unsupervised Spatial Clustering on Stereo Mixtures","display_name":"Bootstrapping Single-channel Source Separation via Unsupervised Spatial Clustering on Stereo Mixtures","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2899647550","doi":"https://doi.org/10.1109/icassp.2019.8683198","mag":"2899647550"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8683198","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8683198","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/1811.02130","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023673004","display_name":"Prem Seetharaman","orcid":null},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Prem Seetharaman","raw_affiliation_strings":["Northwestern University, Evanston, IL, USA","Northwestern university, EVANSTON, IL, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]},{"raw_affiliation_string":"Northwestern university, EVANSTON, IL, USA#TAB#","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086940921","display_name":"Gordon Wichern","orcid":"https://orcid.org/0000-0002-8597-6795"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gordon Wichern","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA","[Mitsubishi Electric Research Laboratories (MERL), Cambridge MA, USA]"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]},{"raw_affiliation_string":"[Mitsubishi Electric Research Laboratories (MERL), Cambridge MA, USA]","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076453358","display_name":"Jonathan Le Roux","orcid":"https://orcid.org/0000-0002-3451-171X"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jonathan Le Roux","raw_affiliation_strings":["Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA","[Mitsubishi Electric Research Laboratories (MERL), Cambridge MA, USA]"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]},{"raw_affiliation_string":"[Mitsubishi Electric Research Laboratories (MERL), Cambridge MA, USA]","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102878078","display_name":"Bryan Pardo","orcid":"https://orcid.org/0000-0002-1427-6492"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bryan Pardo","raw_affiliation_strings":["Northwestern University, Evanston, IL, USA","Northwestern university, EVANSTON, IL, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]},{"raw_affiliation_string":"Northwestern university, EVANSTON, IL, USA#TAB#","institution_ids":["https://openalex.org/I111979921"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5023673004"],"corresponding_institution_ids":["https://openalex.org/I111979921"],"apc_list":null,"apc_paid":null,"fwci":0.1676,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.40706615,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"356","last_page":"360"},"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":0.9988999962806702,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.998199999332428,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/source-separation","display_name":"Source separation","score":0.7645078897476196},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7041112184524536},{"id":"https://openalex.org/keywords/blind-signal-separation","display_name":"Blind signal separation","score":0.6393570303916931},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5834668874740601},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5653905868530273},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5635004043579102},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5547152161598206},{"id":"https://openalex.org/keywords/underdetermined-system","display_name":"Underdetermined system","score":0.5281322598457336},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5021257400512695},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4765779972076416},{"id":"https://openalex.org/keywords/separation","display_name":"Separation (statistics)","score":0.4765099585056305},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42699161171913147},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35601669549942017},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23152273893356323}],"concepts":[{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.7645078897476196},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7041112184524536},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.6393570303916931},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5834668874740601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5653905868530273},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5635004043579102},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5547152161598206},{"id":"https://openalex.org/C179690561","wikidata":"https://www.wikidata.org/wiki/Q4316110","display_name":"Underdetermined system","level":2,"score":0.5281322598457336},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5021257400512695},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4765779972076416},{"id":"https://openalex.org/C2776061190","wikidata":"https://www.wikidata.org/wiki/Q7451805","display_name":"Separation (statistics)","level":2,"score":0.4765099585056305},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42699161171913147},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35601669549942017},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23152273893356323},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icassp.2019.8683198","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8683198","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1811.02130","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1811.02130","pdf_url":"https://arxiv.org/pdf/1811.02130","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":null,"raw_type":"text"},{"id":"mag:2899647550","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/1811.02130","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.1811.02130","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1811.02130","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:1811.02130","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1811.02130","pdf_url":"https://arxiv.org/pdf/1811.02130","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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2899647550.pdf","grobid_xml":"https://content.openalex.org/works/W2899647550.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W141203745","https://openalex.org/W165956390","https://openalex.org/W1866072925","https://openalex.org/W2033178790","https://openalex.org/W2121056381","https://openalex.org/W2129116669","https://openalex.org/W2131049662","https://openalex.org/W2146950091","https://openalex.org/W2149657498","https://openalex.org/W2221409856","https://openalex.org/W2285559681","https://openalex.org/W2315268655","https://openalex.org/W2465050681","https://openalex.org/W2544224704","https://openalex.org/W2552071709","https://openalex.org/W2558649592","https://openalex.org/W2771189179","https://openalex.org/W2795478783","https://openalex.org/W2796992393","https://openalex.org/W2797263747","https://openalex.org/W2889404825","https://openalex.org/W2890244912","https://openalex.org/W2891405874","https://openalex.org/W2892163332","https://openalex.org/W2962715207","https://openalex.org/W2962865004","https://openalex.org/W2962905190","https://openalex.org/W2963218389","https://openalex.org/W2963992487","https://openalex.org/W4235169531","https://openalex.org/W6634460494","https://openalex.org/W6678280073","https://openalex.org/W6679489070","https://openalex.org/W6712300348","https://openalex.org/W6729831399","https://openalex.org/W6750591037"],"related_works":["https://openalex.org/W3014637099","https://openalex.org/W973023320","https://openalex.org/W1982199708","https://openalex.org/W2477548633","https://openalex.org/W2401922685","https://openalex.org/W2208583353","https://openalex.org/W3101375842","https://openalex.org/W2586072182","https://openalex.org/W2691547543","https://openalex.org/W2001875408","https://openalex.org/W2886282615","https://openalex.org/W2408225343","https://openalex.org/W2158240245","https://openalex.org/W2038522539","https://openalex.org/W2945896803","https://openalex.org/W2924874722","https://openalex.org/W2949268173","https://openalex.org/W2184746145","https://openalex.org/W1964312095","https://openalex.org/W2963864522"],"abstract_inverted_index":{"Separating":[0],"an":[1,69,159,188],"audio":[2],"scene":[3,20],"into":[4],"isolated":[5],"sources":[6,45,157],"is":[7,61,145,149],"a":[8,85,112,172],"fundamental":[9],"problem":[10],"in":[11,18,96,158,187],"computer":[12],"audition,":[13],"analogous":[14],"to":[15,83,136,150,155,170],"image":[16],"segmentation":[17],"visual":[19],"analysis.":[21],"Source":[22],"separation":[23,39,73,89,175],"systems":[24,49],"based":[25],"on":[26,53,74],"deep":[27,86],"learning":[28,87],"are":[29,43,50,123],"currently":[30],"the":[31,37,57,99,107,126,131,184],"most":[32],"successful":[33],"approaches":[34,186],"for":[35,177],"solving":[36],"underdetermined":[38],"problem,":[40],"where":[41,56,140],"there":[42],"more":[44],"than":[46],"channels.":[47],"Such":[48],"normally":[51],"trained":[52],"sound":[54],"mixtures":[55,76,82,118,122],"ground":[58],"truth":[59],"decomposition":[60],"already":[62],"known.":[63],"In":[64],"this":[65],"work,":[66],"we":[67,105],"use":[68,151,167],"unsupervised":[70,127,160],"spatial":[71],"source":[72,88,142,174],"stereo":[75],"which":[77,117],"generates":[78],"initial":[79],"decompositions":[80,93],"of":[81,121],"train":[84],"model.":[90],"These":[91],"estimated":[92],"vary":[94],"greatly":[95],"quality":[97],"across":[98],"training":[100,110],"mixtures.":[101],"To":[102],"overcome":[103],"this,":[104],"weight":[106],"data":[108],"during":[109],"using":[111,183],"confidence":[113],"measure":[114],"that":[115,168],"assesses":[116],"or":[119],"parts":[120],"well-separated":[124],"by":[125],"algorithm.":[128],"Once":[129],"trained,":[130],"model":[132,176],"can":[133],"be":[134],"applied":[135],"separate":[137,156],"single-channel":[138],"mixtures,":[139],"no":[141],"direction":[143],"information":[144],"available.":[146],"The":[147],"idea":[148],"simple,":[152],"low-level":[153],"processing":[154],"fashion,":[161],"identify":[162],"easy":[163],"conditions,":[164],"and":[165],"then":[166],"knowledge":[169],"bootstrap":[171],"(self-)supervised":[173],"difficult":[178],"conditions.":[179],"We":[180],"also":[181],"explore":[182],"two":[185],"ensemble.":[189]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}