{"id":"https://openalex.org/W7160666290","doi":"https://doi.org/10.48550/arxiv.2605.06189","title":"Predictive-Generative Drift Decomposition for Speech Enhancement and Separation","display_name":"Predictive-Generative Drift Decomposition for Speech Enhancement and Separation","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160666290","doi":"https://doi.org/10.48550/arxiv.2605.06189"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.06189","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06189","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.2605.06189","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134144156","display_name":"Julius Richter","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Richter, Julius","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135708645","display_name":"Yoshiki Masuyama","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Masuyama, Yoshiki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064522438","display_name":"Christoph Boeddeker","orcid":"https://orcid.org/0000-0002-8701-1567"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Boeddeker, Christoph","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043400469","display_name":"Takahiro Edo","orcid":"https://orcid.org/0000-0003-2011-3339"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Edo, Takahiro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086940921","display_name":"Gordon Wichern","orcid":"https://orcid.org/0000-0002-8597-6795"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wichern, Gordon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135677217","display_name":"Jonathan Le Roux","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roux, Jonathan Le","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":0.9943000078201294,"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":0.9943000078201294,"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/T10283","display_name":"Hearing Loss and Rehabilitation","score":0.0020000000949949026,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/speech-enhancement","display_name":"Speech enhancement","score":0.6341999769210815},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5231999754905701},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.49630001187324524},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.49230000376701355},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.43860000371932983},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.42590001225471497},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.3887999951839447},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.38089999556541443},{"id":"https://openalex.org/keywords/speech-processing","display_name":"Speech processing","score":0.37940001487731934}],"concepts":[{"id":"https://openalex.org/C2776182073","wikidata":"https://www.wikidata.org/wiki/Q7575395","display_name":"Speech enhancement","level":3,"score":0.6341999769210815},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5891000032424927},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5231999754905701},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5081999897956848},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.49630001187324524},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.49230000376701355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44429999589920044},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.43860000371932983},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42590001225471497},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.3887999951839447},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.37940001487731934},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.36320000886917114},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C2776061190","wikidata":"https://www.wikidata.org/wiki/Q7451805","display_name":"Separation (statistics)","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30070000886917114},{"id":"https://openalex.org/C14999030","wikidata":"https://www.wikidata.org/wiki/Q16346","display_name":"Speech synthesis","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C204201278","wikidata":"https://www.wikidata.org/wiki/Q1332614","display_name":"Voice activity detection","level":3,"score":0.28380000591278076},{"id":"https://openalex.org/C99209842","wikidata":"https://www.wikidata.org/wiki/Q643696","display_name":"Speech perception","level":3,"score":0.2825999855995178},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27970001101493835},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.06189","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06189","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.2605.06189","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06189","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":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.7696741819381714}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,167],"propose":[1],"a":[2,15,49,53,58,73,94,102,116,125,156],"plug-and-play":[3],"framework":[4,76,158],"for":[5,25,77,171,198],"speech":[6,17,173,176,199],"enhancement":[7,174],"and":[8,32,39,52,145,163,175,184],"separation":[9,177],"that":[10,105,119,159],"augments":[11],"predictive":[12,38,59],"methods":[13],"with":[14,82],"generative":[16,40,67,87],"prior.":[18],"Our":[19],"approach,":[20],"termed":[21],"Stochastic":[22],"Interpolant":[23],"Prior":[24],"Speech":[26],"(SIPS),":[27],"builds":[28],"on":[29,142],"stochastic":[30,54],"interpolants":[31],"leverages":[33],"their":[34],"flexibility":[35],"to":[36,61,148],"bridge":[37],"modeling.":[41],"Specifically,":[42],"we":[43,92],"decompose":[44],"the":[45,66,83,113,121,129],"interpolation":[46],"dynamics":[47],"into":[48,65],"task-specific":[50],"drift":[51,118],"denoising":[55],"component,":[56],"allowing":[57],"estimate":[60],"be":[62,107],"integrated":[63],"directly":[64],"sampling":[68,122],"process.":[69],"This":[70],"results":[71],"in":[72],"mathematically":[74],"grounded":[75],"combining":[78],"strong":[79],"pretrained":[80],"predictors":[81,150,162,180],"expressive":[84],"power":[85],"of":[86],"models.":[88],"To":[89],"this":[90],"end,":[91],"train":[93],"score":[95,130],"model":[96,131],"using":[97,178],"only":[98],"clean":[99],"speech,":[100],"yielding":[101],"degradation-agnostic":[103],"prior":[104,136],"can":[106],"reused":[108],"across":[109,161],"tasks.":[110,166],"During":[111],"inference,":[112],"predictor":[114],"provides":[115,155],"deterministic":[117],"steers":[120],"process":[123],"toward":[124],"task-consistent":[126],"estimate,":[127],"while":[128],"preserves":[132],"perceptual":[133,191],"naturalness.":[134],"Unlike":[135],"hybrid":[137],"approaches,":[138],"which":[139],"typically":[140],"rely":[141],"architecture-specific":[143],"conditioning":[144],"are":[146],"tied":[147],"particular":[149],"or":[151],"degradation":[152,165],"settings,":[153],"SIPS":[154],"unified":[157],"generalizes":[160],"additive":[164],"demonstrate":[168],"its":[169],"effectiveness":[170],"both":[172],"recent":[179],"such":[181],"as":[182],"SEMamba":[183],"FlexIO.":[185],"The":[186],"proposed":[187],"method":[188],"consistently":[189],"improves":[190],"quality,":[192],"achieving":[193],"gains":[194],"up":[195],"+1.0":[196],"NISQA":[197],"separation.":[200]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-09T00:00:00"}