{"id":"https://openalex.org/W7130430506","doi":"https://doi.org/10.48550/arxiv.2602.15082","title":"S-PRESSO: Ultra Low Bitrate Sound Effect Compression With Diffusion Autoencoders And Offline Quantization","display_name":"S-PRESSO: Ultra Low Bitrate Sound Effect Compression With Diffusion Autoencoders And Offline Quantization","publication_year":2026,"publication_date":"2026-02-16","ids":{"openalex":"https://openalex.org/W7130430506","doi":"https://doi.org/10.48550/arxiv.2602.15082"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.15082","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15082","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.2602.15082","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120026448","display_name":"Zineb Lahrichi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131385","display_name":"Sony (France)","ror":"https://ror.org/03cr99w51","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210131385","https://openalex.org/I4210143797"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Lahrichi, Zineb","raw_affiliation_strings":["IP Paris, SonyAI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IP Paris, SonyAI","institution_ids":["https://openalex.org/I4210131385"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051522702","display_name":"Ga\u00ebtan Hadjeres","orcid":"https://orcid.org/0000-0003-1462-6103"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hadjeres, Ga\u00ebtan","raw_affiliation_strings":["SonyAI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SonyAI","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126320008","display_name":"Ga\u00ebl Richard","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Richard, Ga\u00ebl","raw_affiliation_strings":["IP Paris"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IP Paris","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063097936","display_name":"Geoffroy Peeters","orcid":"https://orcid.org/0000-0001-5255-3019"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peeters, Geoffroy","raw_affiliation_strings":["IP Paris"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IP Paris","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.782800018787384,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.782800018787384,"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":0.06419999897480011,"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/T11349","display_name":"Music Technology and Sound Studies","score":0.05480000004172325,"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/compression","display_name":"Compression (physics)","score":0.5800999999046326},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5683000087738037},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.5645999908447266},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5473999977111816},{"id":"https://openalex.org/keywords/dynamic-range-compression","display_name":"Dynamic range compression","score":0.4749000072479248},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.44179999828338623},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.4146000146865845},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.3889999985694885},{"id":"https://openalex.org/keywords/compression-ratio","display_name":"Compression ratio","score":0.3824000060558319}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7311000227928162},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.5800999999046326},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5683000087738037},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.5645999908447266},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5473999977111816},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.48010000586509705},{"id":"https://openalex.org/C150178126","wikidata":"https://www.wikidata.org/wiki/Q18433212","display_name":"Dynamic range compression","level":2,"score":0.4749000072479248},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.44179999828338623},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.4146000146865845},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3921999931335449},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3889999985694885},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.3824000060558319},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3765999972820282},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3693999946117401},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3612000048160553},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36010000109672546},{"id":"https://openalex.org/C94835093","wikidata":"https://www.wikidata.org/wiki/Q3113333","display_name":"Data compression ratio","level":5,"score":0.3488999903202057},{"id":"https://openalex.org/C57654395","wikidata":"https://www.wikidata.org/wiki/Q1097775","display_name":"Compression artifact","level":5,"score":0.34380000829696655},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.3416999876499176},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.3061000108718872},{"id":"https://openalex.org/C64922751","wikidata":"https://www.wikidata.org/wiki/Q4650799","display_name":"Audio signal","level":3,"score":0.29109999537467957},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C13895895","wikidata":"https://www.wikidata.org/wiki/Q3270773","display_name":"Speech coding","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2702000141143799},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.257099986076355}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.15082","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15082","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.2602.15082","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15082","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":{"Neural":[0],"audio":[1,39,93,148],"compression":[2,8,62,118,135],"models":[3,18],"have":[4,19],"recently":[5],"achieved":[6],"extreme":[7],"rates,":[9,113,136],"enabling":[10],"efficient":[11],"latent":[12,16,87,98],"generative":[13,17,102],"modeling.":[14],"Conversely,":[15],"been":[20],"applied":[21],"to":[22,37,75,90,115],"compression,":[23],"pushing":[24],"the":[25,101,105,126],"limits":[26],"of":[27,104,128],"continuous":[28,67,143],"and":[29,40,68,122,144,152],"discrete":[30,69,145],"approaches.":[31],"However,":[32],"existing":[33],"methods":[34],"remain":[35],"constrained":[36],"low-resolution":[38],"degrade":[41],"substantially":[42],"at":[43,71,125,133],"very":[44],"low":[45,111],"bitrates,":[46,73],"where":[47],"audible":[48],"artifacts":[49],"are":[50],"prominent.":[51],"In":[52],"this":[53],"paper,":[54],"we":[55,108,137],"present":[56],"S-PRESSO,":[57],"a":[58,85,97],"48kHz":[59],"sound":[60],"effect":[61],"model":[63,82,89],"that":[64,139],"produces":[65],"both":[66,142],"embeddings":[70,94],"ultra-low":[72],"down":[74,114],"0.096":[76],"kbps,":[77],"via":[78],"offline":[79],"quantization.":[80],"Our":[81],"relies":[83],"on":[84],"pretrained":[86],"diffusion":[88,106],"decode":[91],"compressed":[92],"learned":[95],"by":[96],"encoder.":[99],"Leveraging":[100],"priors":[103],"decoder,":[107],"achieve":[109],"extremely":[110],"frame":[112],"1Hz":[116],"(750x":[117],"rate),":[119],"producing":[120],"convincing":[121],"realistic":[123],"reconstructions":[124],"cost":[127],"exact":[129],"fidelity.":[130],"Despite":[131],"operating":[132],"high":[134],"demonstrate":[138],"S-PRESSO":[140],"outperforms":[141],"baselines":[146],"in":[147],"quality,":[149],"acoustic":[150],"similarity":[151],"reconstruction":[153],"metrics.":[154]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-02-19T00:00:00"}