{"id":"https://openalex.org/W4312003005","doi":"https://doi.org/10.48550/arxiv.2207.03067","title":"Cross-Scale Vector Quantization for Scalable Neural Speech Coding","display_name":"Cross-Scale Vector Quantization for Scalable Neural Speech Coding","publication_year":2022,"publication_date":"2022-07-07","ids":{"openalex":"https://openalex.org/W4312003005","doi":"https://doi.org/10.48550/arxiv.2207.03067"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2207.03067","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.03067","pdf_url":"https://arxiv.org/pdf/2207.03067","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2207.03067","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100763211","display_name":"Xue Jiang","orcid":"https://orcid.org/0000-0002-1422-9258"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jiang, Xue","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024977056","display_name":"Xiulian Peng","orcid":"https://orcid.org/0000-0001-8213-4878"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Xiulian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031445620","display_name":"Huaying Xue","orcid":"https://orcid.org/0009-0002-7110-5574"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Huaying","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029752627","display_name":"Yuan Zhang","orcid":"https://orcid.org/0000-0002-7909-9845"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100729305","display_name":"Yan L\u00fc","orcid":"https://orcid.org/0009-0006-0580-7745"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Yan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100763211"],"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":0.9975000023841858,"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":0.9975000023841858,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9868000149726868,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9860000014305115,"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/computer-science","display_name":"Computer science","score":0.8332395553588867},{"id":"https://openalex.org/keywords/bitstream","display_name":"Bitstream","score":0.7245892286300659},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6555349826812744},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.606430172920227},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5888586044311523},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.5852099657058716},{"id":"https://openalex.org/keywords/codec","display_name":"Codec","score":0.5842111706733704},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.49012598395347595},{"id":"https://openalex.org/keywords/speech-coding","display_name":"Speech coding","score":0.4641386866569519},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.44680678844451904},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.431215763092041},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41844695806503296},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.38990992307662964},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.36785268783569336},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3540286421775818},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3072666823863983},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.266981303691864},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.23013904690742493}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8332395553588867},{"id":"https://openalex.org/C136695289","wikidata":"https://www.wikidata.org/wiki/Q415568","display_name":"Bitstream","level":3,"score":0.7245892286300659},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6555349826812744},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.606430172920227},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5888586044311523},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.5852099657058716},{"id":"https://openalex.org/C161765866","wikidata":"https://www.wikidata.org/wiki/Q184748","display_name":"Codec","level":2,"score":0.5842111706733704},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.49012598395347595},{"id":"https://openalex.org/C13895895","wikidata":"https://www.wikidata.org/wiki/Q3270773","display_name":"Speech coding","level":2,"score":0.4641386866569519},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.44680678844451904},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.431215763092041},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41844695806503296},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.38990992307662964},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.36785268783569336},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3540286421775818},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3072666823863983},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.266981303691864},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.23013904690742493},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2207.03067","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.03067","pdf_url":"https://arxiv.org/pdf/2207.03067","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2207.03067","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2207.03067","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2207.03067","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.03067","pdf_url":"https://arxiv.org/pdf/2207.03067","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4312003005.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4205607586","https://openalex.org/W2980252933","https://openalex.org/W1994767227","https://openalex.org/W2054991208","https://openalex.org/W4206534210","https://openalex.org/W2610181890","https://openalex.org/W2359242134","https://openalex.org/W2071850723","https://openalex.org/W2374470469","https://openalex.org/W2115984063"],"abstract_inverted_index":{"Bitrate":[0],"scalability":[1],"is":[2,48,86,95],"a":[3,18,60,83,90,122,165],"desirable":[4],"feature":[5,76],"for":[6,30],"audio":[7,14,118],"coding":[8,119],"in":[9,67],"real-time":[10],"communications.":[11],"Existing":[12],"neural":[13,117],"codecs":[15],"usually":[16],"enforce":[17],"specific":[19],"bitrate":[20,128,170],"during":[21],"training,":[22],"so":[23],"different":[24],"models":[25],"need":[26],"to":[27,51,115,126],"be":[28,112],"trained":[29],"each":[31],"target":[32],"bitrate,":[33],"which":[34,68],"increases":[35],"the":[36,40,43,93,100,134,138,146],"memory":[37],"footprint":[38],"at":[39,149,154,159],"sender":[41],"and":[42,46,78,97,157,161],"receiver":[44],"side":[45],"transcoding":[47],"often":[49],"needed":[50],"support":[52],"multiple":[53],"receivers.":[54],"In":[55,80],"this":[56,81],"paper,":[57],"we":[58],"introduce":[59],"cross-scale":[61],"scalable":[62],"vector":[63],"quantization":[64],"scheme":[65,110,136],"(CSVQ),":[66],"multi-scale":[69],"features":[70],"are":[71,105],"encoded":[72],"progressively":[73,98],"with":[74,121,143,169],"stepwise":[75],"fusion":[77],"refinement.":[79],"way,":[82],"coarse-level":[84],"signal":[85],"reconstructed":[87],"if":[88],"only":[89],"portion":[91],"of":[92],"bitstream":[94],"received,":[96],"improves":[99],"quality":[101,167],"as":[102],"more":[103],"bits":[104],"available.":[106],"The":[107],"proposed":[108,135,147],"CSVQ":[109,148],"can":[111],"flexibly":[113],"applied":[114],"any":[116],"network":[120],"mirrored":[123],"auto-encoder":[124],"structure":[125],"achieve":[127],"scalability.":[129,144],"Subjective":[130],"results":[131],"show":[132],"that":[133],"outperforms":[137,152],"classical":[139],"residual":[140],"VQ":[141],"(RVQ)":[142],"Moreover,":[145],"3":[150],"kbps":[151,156],"Opus":[153],"9":[155],"Lyra":[158],"3kbps":[160],"it":[162],"could":[163],"provide":[164],"graceful":[166],"boost":[168],"increase.":[171]},"counts_by_year":[],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2025-10-10T00:00:00"}