{"id":"https://openalex.org/W7160938201","doi":"https://doi.org/10.48550/arxiv.2605.10084","title":"PoDAR: Power-Disentangled Audio Representation for Generative Modeling","display_name":"PoDAR: Power-Disentangled Audio Representation for Generative Modeling","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7160938201","doi":"https://doi.org/10.48550/arxiv.2605.10084"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.10084","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10084","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.10084","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030815835","display_name":"Alejandro Luebs","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luebs, Alejandro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001928203","display_name":"Mithilesh Vaidya","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vaidya, Mithilesh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112936120","display_name":"Ishaan Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumar, Ishaan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016409706","display_name":"Sumukh Badam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Badam, Sumukh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048740689","display_name":"Stephen W. Bailey","orcid":"https://orcid.org/0000-0001-7228-1823"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bailey, Stephen W.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135975345","display_name":"Matthew Bendel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bendel, Matthew","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135972675","display_name":"Jose Sotelo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sotelo, Jose","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5014723468","display_name":"Xingzhe He","orcid":"https://orcid.org/0000-0002-3460-4879"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Xingzhe","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/T10201","display_name":"Speech Recognition and Synthesis","score":0.43790000677108765,"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"}},"topics":[{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.43790000677108765,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.18029999732971191,"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"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.07150000333786011,"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/audio-signal","display_name":"Audio signal","score":0.6013000011444092},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.54830002784729},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.5196999907493591},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.450300008058548},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.4239000082015991},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.423799991607666},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.41589999198913574},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4065999984741211}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7138000130653381},{"id":"https://openalex.org/C64922751","wikidata":"https://www.wikidata.org/wiki/Q4650799","display_name":"Audio signal","level":3,"score":0.6013000011444092},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.54830002784729},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.5196999907493591},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.48240000009536743},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4805999994277954},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.450300008058548},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.4239000082015991},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.423799991607666},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.41589999198913574},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4065999984741211},{"id":"https://openalex.org/C127220857","wikidata":"https://www.wikidata.org/wiki/Q2719318","display_name":"Audio signal processing","level":4,"score":0.3790000081062317},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.2671999931335449}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.10084","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10084","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.10084","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10084","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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],"performance":[1],"of":[2,16,37,93,150],"audio":[3,38],"latent":[4,19,43,68,83],"diffusion":[5],"models":[6,96],"is":[7],"primarily":[8,26],"governed":[9],"by":[10,131,135],"generator":[11],"expressivity":[12],"and":[13,67,97,133],"the":[14,17,28,34,82,91,138,148,158],"modelability":[15,44],"underlying":[18],"space.":[20],"While":[21],"recent":[22],"research":[23],"has":[24],"focused":[25],"on":[27,137],"former,":[29],"as":[30,32],"well":[31],"improving":[33],"reconstruction":[35],"fidelity":[36],"codecs,":[39],"we":[40],"demonstrate":[41],"that":[42,61],"can":[45],"be":[46],"significantly":[47],"improved":[48],"through":[49],"explicit":[50],"factor":[51],"disentanglement.":[52],"We":[53],"present":[54],"PoDAR":[55,114],"(Power-Disentangled":[56],"Audio":[57,107],"Representation),":[58],"a":[59,63,105,117],"framework":[60],"utilizes":[62],"randomized":[64],"power":[65,74,143],"augmentation":[66],"consistency":[69],"objective":[70],"to":[71,86,104,122,153,162],"decouple":[72],"signal":[73],"from":[75],"invariant":[76],"semantic":[77],"content.":[78],"This":[79],"factorization":[80],"makes":[81],"space":[84],"easier":[85],"model,":[87],"which":[88],"both":[89],"accelerates":[90],"convergence":[92,121],"downstream":[94],"generative":[95],"improves":[98],"final":[99,128],"overall":[100],"performance.":[101],"When":[102],"applied":[103],"Stable":[106],"1.0":[108],"VAE":[109],"with":[110],"an":[111],"F5-TTS":[112],"generator,":[113],"achieves":[115],"about":[116],"$2\\times$":[118],"acceleration":[119],"in":[120],"match":[123],"baseline":[124],"performance,":[125],"while":[126],"increasing":[127],"speaker":[129],"similarity":[130],"0.055":[132],"UTMOS":[134],"0.22":[136],"LibriSpeech-PC":[139],"dataset.":[140],"Furthermore,":[141],"isolating":[142],"into":[144],"dedicated":[145],"channels":[146],"enables":[147],"application":[149],"CFG":[151],"exclusively":[152],"power-invariant":[154],"content,":[155],"effectively":[156],"extending":[157],"stable":[159],"guidance":[160],"regime":[161],"higher":[163],"scales.":[164]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}
