{"id":"https://openalex.org/W6948459793","doi":"https://doi.org/10.5281/zenodo.10265389","title":"Self-Refining of Pseudo Labels for Music Source Separation With Noisy Labeled Data","display_name":"Self-Refining of Pseudo Labels for Music Source Separation With Noisy Labeled Data","publication_year":2023,"publication_date":"2023-11-04","ids":{"openalex":"https://openalex.org/W6948459793","doi":"https://doi.org/10.5281/zenodo.10265389"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.10265389","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.10265389","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"ConferencePaper"},"type":"conference-paper","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.10265389","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Junghyun Koo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junghyun Koo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yunkee Chae","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yunkee Chae","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chang-Bin Jeon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang-Bin Jeon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Kyogu Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kyogu Lee","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"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":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.09920000284910202,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.09920000284910202,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10236","display_name":"Aquatic Ecosystems and Phytoplankton Dynamics","score":0.06729999929666519,"subfield":{"id":"https://openalex.org/subfields/2304","display_name":"Environmental Chemistry"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13568","display_name":"Wood and Agarwood Research","score":0.05530000105500221,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6744999885559082},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5831999778747559},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5325999855995178},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.4677000045776367},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.44859999418258667},{"id":"https://openalex.org/keywords/source-separation","display_name":"Source separation","score":0.41620001196861267}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7092999815940857},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6998999714851379},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6744999885559082},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5831999778747559},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5325999855995178},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.4677000045776367},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.44859999418258667},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.41620001196861267},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4131999909877777},{"id":"https://openalex.org/C2776061190","wikidata":"https://www.wikidata.org/wiki/Q7451805","display_name":"Separation (statistics)","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.33000001311302185},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.31130000948905945},{"id":"https://openalex.org/C2983685735","wikidata":"https://www.wikidata.org/wiki/Q5227355","display_name":"Data source","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2865999937057495}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5281/zenodo.10265389","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.10265389","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"ConferencePaper"}],"best_oa_location":{"id":"doi:10.5281/zenodo.10265389","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.10265389","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"ConferencePaper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Music":[0],"source":[1],"separation":[2],"(MSS)":[3],"faces":[4],"challenges":[5],"due":[6],"to":[7,56,87,90,98],"limited":[8],"availability":[9],"and":[10,77],"potential":[11],"noise":[12],"in":[13,32,45],"correctly":[14],"labeled":[15],"individual":[16],"instrument":[17,30,53],"tracks.":[18],"In":[19],"this":[20],"paper,":[21],"we":[22],"propose":[23],"an":[24],"automated":[25],"approach":[26],"for":[27,51,73,84],"refining":[28,70],"mislabeled":[29],"tracks":[31],"a":[33,47,57,61,91,99,116],"partially":[34],"noisy-labeled":[35,42,71],"dataset.":[36,63,93],"The":[37,64],"proposed":[38],"self-refining":[39],"technique":[40],"with":[41,60,115],"dataset":[43,83],"results":[44,89],"only":[46,96],"1%":[48],"accuracy":[49],"degradation":[50],"multi-label":[52],"recognition":[54],"compared":[55],"classifier":[58,117],"trained":[59,104,111,118],"clean-labeled":[62,92],"study":[65],"demonstrates":[66],"the":[67,81],"importance":[68],"of":[69],"data":[72],"training":[74],"MSS":[75,85,102],"models":[76,103],"shows":[78],"that":[79],"utilizing":[80],"refined":[82,114],"leads":[86],"comparable":[88],"Notably,":[94],"upon":[95],"access":[97],"noisy":[100],"dataset,":[101],"on":[105,112,119],"self-refined":[106],"datasets":[107,113],"even":[108],"outperformed":[109],"those":[110],"clean":[120],"labels.":[121]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}