{"id":"https://openalex.org/W4415097458","doi":"https://doi.org/10.48550/arxiv.2510.06785","title":"Moises-Light: Resource-efficient Band-split U-Net For Music Source Separation","display_name":"Moises-Light: Resource-efficient Band-split U-Net For Music Source Separation","publication_year":2025,"publication_date":"2025-10-08","ids":{"openalex":"https://openalex.org/W4415097458","doi":"https://doi.org/10.48550/arxiv.2510.06785"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2510.06785","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.06785","pdf_url":"https://arxiv.org/pdf/2510.06785","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.06785","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119964070","display_name":"Yun-Ning","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yun-Ning","raw_affiliation_strings":["Amy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amy","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119964071","display_name":"Hung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040125108","display_name":"Igor Gadelha Pereira","orcid":"https://orcid.org/0000-0001-7539-4663"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pereira, Igor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5047928537","display_name":"Filip Korzeniowski","orcid":"https://orcid.org/0000-0002-7287-8053"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Korzeniowski, Filip","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":true,"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":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9958000183105469,"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":0.9958000183105469,"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":0.9941999912261963,"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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/scalability","display_name":"Scalability","score":0.680400013923645},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.48899999260902405},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.43720000982284546},{"id":"https://openalex.org/keywords/source-separation","display_name":"Source separation","score":0.42719998955726624},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.33059999346733093}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7250999808311462},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.680400013923645},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.48899999260902405},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.43720000982284546},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.42719998955726624},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40119999647140503},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3077999949455261},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3066999912261963},{"id":"https://openalex.org/C3018397939","wikidata":"https://www.wikidata.org/wiki/Q3644502","display_name":"Open source","level":3,"score":0.29899999499320984},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26739999651908875},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2510.06785","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.06785","pdf_url":"https://arxiv.org/pdf/2510.06785","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.06785","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.06785","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.06785","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.06785","pdf_url":"https://arxiv.org/pdf/2510.06785","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415097458.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,69],"recent":[1,77],"years,":[2],"significant":[3,34],"advances":[4,78],"have":[5,57],"been":[6,58],"made":[7],"in":[8,46,113],"music":[9],"source":[10],"separation,":[11],"with":[12,42,87,99],"model":[13,92,125],"architectures":[14],"such":[15],"as":[16,133],"dual-path":[17],"modeling,":[18],"band-split":[19],"modules,":[20],"or":[21],"transformer":[22],"layers":[23],"achieving":[24],"comparably":[25],"good":[26],"results.":[27],"However,":[28],"these":[29,76],"models":[30,56,98],"often":[31],"contain":[32],"a":[33,81,90],"number":[35],"of":[36,48],"parameters,":[37],"posing":[38],"challenges":[39],"to":[40,65,79,97,101],"devices":[41],"limited":[43],"computational":[44],"resources":[45],"terms":[47],"training":[49,135],"and":[50],"practical":[51],"application.":[52],"While":[53],"some":[54],"lightweight":[55,82,91],"introduced,":[59],"they":[60],"generally":[61],"perform":[62],"worse":[63],"compared":[64],"their":[66],"larger":[67],"counterparts.":[68],"this":[70],"paper,":[71],"we":[72],"take":[73],"inspiration":[74],"from":[75],"improve":[80],"model.":[83],"We":[84],"demonstrate":[85],"that":[86],"careful":[88],"design,":[89],"can":[93],"achieve":[94],"comparable":[95],"SDRs":[96],"up":[100],"13":[102],"times":[103],"more":[104],"parameters.":[105],"Our":[106],"proposed":[107,124],"model,":[108],"Moises-Light,":[109],"achieves":[110],"competitive":[111,128],"results":[112],"separating":[114],"four":[115],"musical":[116],"stems":[117],"on":[118],"the":[119],"MUSDB-HQ":[120],"benchmark":[121],"dataset.":[122],"The":[123],"also":[126],"demonstrates":[127],"scalability":[129],"when":[130],"using":[131],"MoisesDB":[132],"additional":[134],"data.":[136]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-13T00:00:00"}