乐胖代购免代理版

{"id":"https://openalex.org/W4319862245","doi":"https://doi.org/10.1109/slt54892.2023.10022496","title":"Wavefit: an Iterative and Non-Autoregressive Neural Vocoder Based on Fixed-Point Iteration","display_name":"Wavefit: an Iterative and Non-Autoregressive Neural Vocoder Based on Fixed-Point Iteration","publication_year":2023,"publication_date":"2023-01-09","ids":{"openalex":"https://openalex.org/W4319862245","doi":"https://doi.org/10.1109/slt54892.2023.10022496"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/slt54892.2023.10022496","pdf_url":null,"source":{"id":"https://openalex.org/S4363605953","display_name":"2022 IEEE Spoken Language Technology Workshop (SLT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2210.01029","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101503856","display_name":"Yuma Koizumi","orcid":"https://orcid.org/0000-0003-3645-6213"},"institutions":[],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuma Koizumi","raw_affiliation_strings":["Google Research, Japan"],"affiliations":[{"raw_affiliation_string":"Google Research, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034837951","display_name":"Kohei Yatabe","orcid":"https://orcid.org/0000-0002-1345-0663"},"institutions":[{"id":"https://openalex.org/I92614990","display_name":"Tokyo University of Agriculture and Technology","ror":"https://ror.org/00qg0kr10","country_code":"JP","type":"education","lineage":["https://openalex.org/I92614990"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kohei Yatabe","raw_affiliation_strings":["Tokyo University of Agriculture and Technology, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo University of Agriculture and Technology, Japan","institution_ids":["https://openalex.org/I92614990"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003420204","display_name":"Heiga Zen","orcid":"https://orcid.org/0000-0002-8959-5471"},"institutions":[],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Heiga Zen","raw_affiliation_strings":["Google Research, Japan"],"affiliations":[{"raw_affiliation_string":"Google Research, Japan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059692696","display_name":"Michiel Bacchiani","orcid":null},"institutions":[],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Michiel Bacchiani","raw_affiliation_strings":["Google Research, Japan"],"affiliations":[{"raw_affiliation_string":"Google Research, Japan","institution_ids":[]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.473,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.999754,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"884","last_page":"891"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9997,"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":0.9997,"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/T11309","display_name":"Music and Audio Processing","score":0.9994,"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/T10860","display_name":"Speech and Audio Processing","score":0.9989,"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":[],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.77138364},{"id":"https://openalex.org/C134537474","wikidata":"https://www.wikidata.org/wiki/Q17144832","display_name":"Naturalness","level":2,"score":0.6033269},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.56076056},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.53898036},{"id":"https://openalex.org/C61445026","wikidata":"https://www.wikidata.org/wiki/Q217608","display_name":"Fixed point","level":2,"score":0.49526015},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4940758},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.48384824},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4292712},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4261728},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.41771123},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37394437},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15317595},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/slt54892.2023.10022496","pdf_url":null,"source":{"id":"https://openalex.org/S4363605953","display_name":"2022 IEEE Spoken Language Technology Workshop (SLT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2210.01029","pdf_url":"https://arxiv.org/pdf/2210.01029","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2210.01029","pdf_url":"https://arxiv.org/pdf/2210.01029","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":46,"referenced_works":["https://openalex.org/W205960364","https://openalex.org/W2111395440","https://openalex.org/W2246091536","https://openalex.org/W2519091744","https://openalex.org/W2749651610","https://openalex.org/W2775336875","https://openalex.org/W2911317657","https://openalex.org/W2962911378","https://openalex.org/W2963091184","https://openalex.org/W2963300588","https://openalex.org/W2964215687","https://openalex.org/W2964243274","https://openalex.org/W2972359262","https://openalex.org/W2972495969","https://openalex.org/W2972519044","https://openalex.org/W2998498479","https://openalex.org/W3015338123","https://openalex.org/W3047002475","https://openalex.org/W3091928890","https://openalex.org/W3095095816","https://openalex.org/W3096159803","https://openalex.org/W3096709315","https://openalex.org/W3097945073","https://openalex.org/W3098557217","https://openalex.org/W3118064383","https://openalex.org/W3144035034","https://openalex.org/W3161296985","https://openalex.org/W3162075194","https://openalex.org/W3180374548","https://openalex.org/W3197273793","https://openalex.org/W3197324626","https://openalex.org/W3198234802","https://openalex.org/W3200625292","https://openalex.org/W3203491020","https://openalex.org/W3215615641","https://openalex.org/W4200483526","https://openalex.org/W4221152438","https://openalex.org/W4221152471","https://openalex.org/W4221161734","https://openalex.org/W4224931676","https://openalex.org/W4225566824","https://openalex.org/W4226063663","https://openalex.org/W4241652050","https://openalex.org/W4281736089","https://openalex.org/W4287889585","https://openalex.org/W4382202681"],"related_works":["https://openalex.org/W4387098302","https://openalex.org/W4288365855","https://openalex.org/W3165080709","https://openalex.org/W2948317131","https://openalex.org/W2945105049","https://openalex.org/W2626699140","https://openalex.org/W2111961547","https://openalex.org/W2029561777","https://openalex.org/W172797710","https://openalex.org/W1554502231"],"abstract_inverted_index":{"Denoising":[0],"diffusion":[1],"probabilistic":[2],"models":[3,13],"(DDPMs)":[4],"and":[5,19,29,38,65,99],"generative":[6,12],"adversarial":[7,30,75],"networks":[8],"(GANs)":[9],"are":[10,123],"popular":[11],"for":[14,72],"neural":[15,40,69],"vocoders.":[16],"The":[17],"DDPMs":[18],"GANs":[20,49],"can":[21],"be":[22],"characterized":[23],"by":[24,102],"the":[25,46,108],"iterative":[26,53],"denoising":[27],"framework":[28,54],"training,":[31],"respectively.":[32],"This":[33],"study":[34],"proposes":[35],"a":[36,51,67],"fast":[37],"high-quality":[39],"vocoder":[41],"called":[42],"WaveFit,":[43],"which":[44],"integrates":[45],"essence":[47],"of":[48,111],"into":[50],"DDPM-like":[52],"based":[55],"on":[56],"fixed-point":[57],"iteration.":[58],"WaveFit":[59,103,112],"iteratively":[60],"denoises":[61],"an":[62,74],"input":[63],"signal,":[64],"trains":[66],"deep":[68],"network":[70],"(DNN)":[71],"minimizing":[73],"loss":[76],"calculated":[77],"from":[78],"intermediate":[79],"outputs":[80],"at":[81,125],"all":[82],"iterations.":[83,106],"Subjective":[84],"(side-by-side)":[85],"listening":[86],"tests":[87],"showed":[88],"no":[89],"statistically":[90],"significant":[91],"differences":[92],"in":[93],"naturalness":[94],"between":[95],"human":[96],"natural":[97],"speech":[98],"those":[100],"synthesized":[101],"with":[104],"five":[105],"Furthermore,":[107],"inference":[109],"speed":[110],"was":[113],"more":[114],"than":[115,119],"240":[116],"times":[117],"faster":[118],"WaveRNN.":[120],"Audio":[121],"demos":[122],"available":[124],"google.github.io/df-conformer/wavefit/.":[126]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4319862245","counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-01-02T11:21:42.305527","created_date":"2023-02-11"}