{"id":"https://openalex.org/W7163590457","doi":"https://doi.org/10.48550/arxiv.2606.04921","title":"SURF: Separation via Unsupervised Remixing Flow","display_name":"SURF: Separation via Unsupervised Remixing Flow","publication_year":2026,"publication_date":"2026-06-03","ids":{"openalex":"https://openalex.org/W7163590457","doi":"https://doi.org/10.48550/arxiv.2606.04921"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.04921","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04921","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.04921","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137915694","display_name":"Henry Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Henry","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020401831","display_name":"Robin Scheibler","orcid":"https://orcid.org/0000-0002-5205-8365"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Scheibler, Robin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016864112","display_name":"Efthymios Tzinis","orcid":"https://orcid.org/0000-0002-1047-1338"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tzinis, Efthymios","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080404214","display_name":"Matt Shannon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shannon, Matt","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137915615","display_name":"Arnaud Doucet","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Doucet, Arnaud","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5112763337","display_name":"John R. Hershey","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hershey, John R.","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":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9370999932289124,"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":0.9370999932289124,"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":0.023600000888109207,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.00800000037997961,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/unsupervised-learning","display_name":"Unsupervised learning","score":0.5964000225067139},{"id":"https://openalex.org/keywords/source-separation","display_name":"Source separation","score":0.565500020980835},{"id":"https://openalex.org/keywords/blind-signal-separation","display_name":"Blind signal separation","score":0.5609999895095825},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.5489000082015991},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5443999767303467},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4580000042915344},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.41760000586509705},{"id":"https://openalex.org/keywords/separation","display_name":"Separation (statistics)","score":0.38609999418258667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7286999821662903},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5964000225067139},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.565500020980835},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.5609999895095825},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.5489000082015991},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5443999767303467},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5321000218391418},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4580000042915344},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.41760000586509705},{"id":"https://openalex.org/C2776061190","wikidata":"https://www.wikidata.org/wiki/Q7451805","display_name":"Separation (statistics)","level":2,"score":0.38609999418258667},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3790999948978424},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3779999911785126},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.349700003862381},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C2780551164","wikidata":"https://www.wikidata.org/wiki/Q2306599","display_name":"Column (typography)","level":3,"score":0.2777000069618225},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.04921","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04921","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.04921","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04921","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":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"The":[0],"goal":[1],"of":[2,20,95,122],"single-channel":[3],"source":[4,22,46,80],"separation":[5,81],"is":[6,48],"to":[7,43,59,118,146],"reconstruct":[8],"$K$":[9],"sources":[10],"given":[11],"their":[12],"mixture.":[13],"In":[14],"supervised":[15,55,97],"settings":[16],"where":[17],"vast":[18],"amounts":[19],"clean":[21,45],"data":[23],"are":[24,57],"available,":[25,54],"this":[26,64,139],"challenging,":[27],"ill-posed":[28],"problem":[29],"has":[30],"been":[31],"addressed":[32],"successfully":[33],"by":[34,138],"generative":[35],"diffusion":[36],"and":[37,51,100,141,154],"flow-based":[38],"prior":[39],"models.":[40],"However,":[41],"access":[42],"such":[44],"samples":[47],"often":[49],"limited,":[50],"even":[52],"when":[53],"models":[56],"vulnerable":[58],"domain":[60],"shifts.":[61],"To":[62],"bridge":[63],"gap,":[65],"we":[66,113],"present":[67],"Separation":[68],"via":[69],"Unsupervised":[70],"Remixing":[71],"Flow":[72],"(SURF),":[73],"an":[74],"unsupervised":[75,167],"flow":[76,98,125],"matching":[77,99],"approach":[78,140],"for":[79,173],"that":[82,158],"learns":[83],"directly":[84],"from":[85,109,127],"observed":[86],"mixtures.":[87],"This":[88],"method":[89],"relies":[90],"on":[91,152],"a":[92,105,110,115,123,143,161],"novel":[93,144],"combination":[94],"state-of-the-art":[96],"regression-based":[101],"self-supervised":[102],"techniques.":[103],"At":[104],"high":[106],"level,":[107],"starting":[108],"teacher":[111],"model,":[112],"utilize":[114],"\"remixing\"":[116],"step":[117],"bootstrap":[119],"the":[120,128,135,147],"learning":[121],"student":[124],"model":[126],"teacher's":[129],"estimates.":[130],"We":[131],"provide":[132],"insights":[133],"into":[134],"objectives":[136],"optimized":[137],"draw":[142],"connection":[145],"Wake-Sleep":[148],"algorithm.":[149],"Empirical":[150],"evaluations":[151],"image":[153],"audio":[155],"benchmarks":[156],"demonstrate":[157],"SURF":[159],"establishes":[160],"new":[162],"state-of-the-art,":[163],"significantly":[164],"outperforming":[165],"existing":[166],"methods.":[168],"See":[169],"our":[170],"demo":[171],"page":[172],"examples.":[174],"https://google.github.io/df-conformer/surf/":[175]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-05T00:00:00"}
