{"id":"https://openalex.org/W3212619684","doi":"https://doi.org/10.1109/mlsp52302.2021.9596428","title":"Singing Fundamental Frequency Contour Generation Using Generalized Command-Response Model and Score-Conditional Variational Autoencoder","display_name":"Singing Fundamental Frequency Contour Generation Using Generalized Command-Response Model and Score-Conditional Variational Autoencoder","publication_year":2021,"publication_date":"2021-10-25","ids":{"openalex":"https://openalex.org/W3212619684","doi":"https://doi.org/10.1109/mlsp52302.2021.9596428","mag":"3212619684"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp52302.2021.9596428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp52302.2021.9596428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101792863","display_name":"Shogo Seki","orcid":"https://orcid.org/0000-0001-8284-188X"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shogo Seki","raw_affiliation_strings":["NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042876055","display_name":"Haruka Taga","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haruka Taga","raw_affiliation_strings":["Graduate School of Informatics"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Informatics","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078330211","display_name":"Tomoki Toda","orcid":"https://orcid.org/0000-0001-8146-1279"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoki Toda","raw_affiliation_strings":["Information Technology Center, Nagoya University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Technology Center, Nagoya University","institution_ids":["https://openalex.org/I60134161"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":"1","last_page":"3"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9998000264167786,"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/T11309","display_name":"Music and Audio Processing","score":0.9998000264167786,"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":0.9997000098228455,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9984999895095825,"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/autoencoder","display_name":"Autoencoder","score":0.839290976524353},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.696684718132019},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6713215708732605},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5690305233001709},{"id":"https://openalex.org/keywords/singing","display_name":"Singing","score":0.5625304579734802},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.49901342391967773},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.46221867203712463},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46207761764526367},{"id":"https://openalex.org/keywords/fundamental-frequency","display_name":"Fundamental frequency","score":0.41093355417251587},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.19282177090644836},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.10614505410194397},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.05726173520088196}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.839290976524353},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.696684718132019},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6713215708732605},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5690305233001709},{"id":"https://openalex.org/C44819458","wikidata":"https://www.wikidata.org/wiki/Q27939","display_name":"Singing","level":2,"score":0.5625304579734802},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.49901342391967773},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.46221867203712463},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46207761764526367},{"id":"https://openalex.org/C10513763","wikidata":"https://www.wikidata.org/wiki/Q1331774","display_name":"Fundamental frequency","level":2,"score":0.41093355417251587},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.19282177090644836},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.10614505410194397},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.05726173520088196},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp52302.2021.9596428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp52302.2021.9596428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5131028598","display_name":"Cross-disciplinary research on the prediction and control of real-world interactions based on evidence and causality","funder_award_id":"21H04892","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G7789893098","display_name":null,"funder_award_id":"JP-MJCR19A3","funder_id":"https://openalex.org/F4320338075","funder_display_name":"Core Research for Evolutional Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"},{"id":"https://openalex.org/F4320338075","display_name":"Core Research for Evolutional Science and Technology","ror":"https://ror.org/00097mb19"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1549618637","https://openalex.org/W1959608418","https://openalex.org/W2022125261","https://openalex.org/W2072389495","https://openalex.org/W2108501770","https://openalex.org/W2120605154","https://openalex.org/W2128653836","https://openalex.org/W2150933458","https://openalex.org/W2170667735","https://openalex.org/W2187234408","https://openalex.org/W2321061684","https://openalex.org/W2471520273","https://openalex.org/W2519648275","https://openalex.org/W2563698958","https://openalex.org/W2567070169","https://openalex.org/W2753719838","https://openalex.org/W2786905999","https://openalex.org/W2891535489","https://openalex.org/W2902967674","https://openalex.org/W2938023232","https://openalex.org/W2963970792","https://openalex.org/W2964121744","https://openalex.org/W2991097512","https://openalex.org/W3114898335","https://openalex.org/W6631190155","https://openalex.org/W6640963894","https://openalex.org/W6675944832","https://openalex.org/W6731370813","https://openalex.org/W6770364479"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2390529913","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2142368101","https://openalex.org/W2734887215","https://openalex.org/W2803255133"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,78,92,100],"method":[4,75,120],"for":[5,22],"achieving":[6],"physically":[7,44],"motivated":[8],"and":[9,97,108,133],"interpretable":[10],"control":[11,138],"of":[12,59,91,130],"fundamental":[13],"frequency":[14],"(F0)":[15],"contour":[16,87],"generation":[17],"in":[18,52],"singing":[19,34],"aid":[20],"systems":[21],"laryngectomees.":[23],"Recently":[24],"proposed":[25,74,119],"variational":[26],"autoencoder":[27],"(VAE)-based":[28],"method,":[29],"VAE-SPACE,":[30],"has":[31],"successfully":[32],"generated":[33],"F0":[35,46,53,86,94,139],"contours":[36,140],"from":[37],"musical":[38,67,106],"scores.":[39,68],"However,":[40],"VAE-SPACE":[41,55,125],"can":[42],"generate":[43],"deviated":[45],"contours.":[47],"Moreover,":[48],"to":[49,83,104,137],"represent":[50,84],"fluctuations":[51],"contours,":[54],"requires":[56],"manual":[57,128],"adjustment":[58,129],"noise":[60,110,131],"components":[61,111,132],"used":[62],"as":[63,88,124],"the":[64,73,109,118,127,145],"input":[65],"with":[66],"To":[69],"address":[70],"these":[71],"issues,":[72],"1)":[76],"introduces":[77],"generalized":[79],"command-response":[80],"(GCR)":[81],"model":[82],"an":[85,89],"approximation":[90],"physical":[93],"production":[95],"mechanism,":[96],"2)":[98],"employs":[99],"conditional":[101],"VAE":[102],"(CVAE)":[103],"treat":[105],"scores":[107],"separately.":[112],"The":[113],"experimental":[114],"results":[115],"reveal":[116],"that":[117],"achieves":[121],"comparable":[122],"performance":[123],"without":[126],"makes":[134],"it":[135],"possible":[136],"more":[141],"intuitively":[142],"by":[143],"using":[144],"trained":[146],"GCR":[147],"model.":[148]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}