{"id":"https://openalex.org/W2996343525","doi":"https://doi.org/10.1109/tencon.2019.8929436","title":"Sub-band Vector Quantized Variational AutoEncoder for Spectral Envelope Quantization","display_name":"Sub-band Vector Quantized Variational AutoEncoder for Spectral Envelope Quantization","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2996343525","doi":"https://doi.org/10.1109/tencon.2019.8929436","mag":"2996343525"},"language":"en","primary_location":{"id":"doi:10.1109/tencon.2019.8929436","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929436","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"},"type":"article","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/A5053190423","display_name":"Tanasan Srikotr","orcid":null},"institutions":[{"id":"https://openalex.org/I171481255","display_name":"Shibaura Institute of Technology","ror":"https://ror.org/020wjcq07","country_code":"JP","type":"education","lineage":["https://openalex.org/I171481255"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Tanasan Srikotr","raw_affiliation_strings":["Division of Funtional Control Systems, Shibaura Institute of Technology, Japan"],"affiliations":[{"raw_affiliation_string":"Division of Funtional Control Systems, Shibaura Institute of Technology, Japan","institution_ids":["https://openalex.org/I171481255"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082062172","display_name":"Kazunori Mano","orcid":null},"institutions":[{"id":"https://openalex.org/I171481255","display_name":"Shibaura Institute of Technology","ror":"https://ror.org/020wjcq07","country_code":"JP","type":"education","lineage":["https://openalex.org/I171481255"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazunori Mano","raw_affiliation_strings":["Division of Funtional Control Systems, Shibaura Institute of Technology, Japan"],"affiliations":[{"raw_affiliation_string":"Division of Funtional Control Systems, Shibaura Institute of Technology, Japan","institution_ids":["https://openalex.org/I171481255"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5053190423"],"corresponding_institution_ids":["https://openalex.org/I171481255"],"apc_list":null,"apc_paid":null,"fwci":0.14,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.59240644,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"296","last_page":"300"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9980999827384949,"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":0.9980999827384949,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9966999888420105,"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":0.9944000244140625,"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/autoencoder","display_name":"Autoencoder","score":0.8820424675941467},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.8233640789985657},{"id":"https://openalex.org/keywords/linde\u2013buzo\u2013gray-algorithm","display_name":"Linde\u2013Buzo\u2013Gray algorithm","score":0.7477767467498779},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.7274332642555237},{"id":"https://openalex.org/keywords/spectral-envelope","display_name":"Spectral envelope","score":0.5915939807891846},{"id":"https://openalex.org/keywords/learning-vector-quantization","display_name":"Learning vector quantization","score":0.5570898652076721},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5502781271934509},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4906240999698639},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46468356251716614},{"id":"https://openalex.org/keywords/envelope","display_name":"Envelope (radar)","score":0.4281007647514343},{"id":"https://openalex.org/keywords/buoy","display_name":"Buoy","score":0.42789602279663086},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.40214401483535767},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.38775214552879333},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36462777853012085},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.35551464557647705},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.16255390644073486},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13309311866760254}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8820424675941467},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.8233640789985657},{"id":"https://openalex.org/C93372532","wikidata":"https://www.wikidata.org/wiki/Q6552455","display_name":"Linde\u2013Buzo\u2013Gray algorithm","level":3,"score":0.7477767467498779},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.7274332642555237},{"id":"https://openalex.org/C54926389","wikidata":"https://www.wikidata.org/wiki/Q7575188","display_name":"Spectral envelope","level":2,"score":0.5915939807891846},{"id":"https://openalex.org/C40567965","wikidata":"https://www.wikidata.org/wiki/Q1820283","display_name":"Learning vector quantization","level":3,"score":0.5570898652076721},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5502781271934509},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4906240999698639},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46468356251716614},{"id":"https://openalex.org/C65155139","wikidata":"https://www.wikidata.org/wiki/Q5380912","display_name":"Envelope (radar)","level":3,"score":0.4281007647514343},{"id":"https://openalex.org/C2779847632","wikidata":"https://www.wikidata.org/wiki/Q30026","display_name":"Buoy","level":2,"score":0.42789602279663086},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40214401483535767},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.38775214552879333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36462777853012085},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.35551464557647705},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.16255390644073486},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13309311866760254},{"id":"https://openalex.org/C199104240","wikidata":"https://www.wikidata.org/wiki/Q118291","display_name":"Marine engineering","level":1,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon.2019.8929436","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929436","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1544785557","https://openalex.org/W1959608418","https://openalex.org/W1973681148","https://openalex.org/W2025768430","https://openalex.org/W2082144929","https://openalex.org/W2099471712","https://openalex.org/W2100495367","https://openalex.org/W2105728138","https://openalex.org/W2127218421","https://openalex.org/W2134383396","https://openalex.org/W2141905484","https://openalex.org/W2218318129","https://openalex.org/W2296581541","https://openalex.org/W2405774341","https://openalex.org/W2471520273","https://openalex.org/W2519091744","https://openalex.org/W2527729766","https://openalex.org/W2752796333","https://openalex.org/W2766736793","https://openalex.org/W2804145368","https://openalex.org/W2884607399","https://openalex.org/W2892009954","https://openalex.org/W2901123912","https://openalex.org/W2949382160","https://openalex.org/W2963134713","https://openalex.org/W2963341071","https://openalex.org/W2963411289","https://openalex.org/W2963799213","https://openalex.org/W2963975282","https://openalex.org/W3100270690","https://openalex.org/W3106114001","https://openalex.org/W3125709657","https://openalex.org/W4293568373","https://openalex.org/W4320013936","https://openalex.org/W6675897241","https://openalex.org/W6678914141","https://openalex.org/W6682610290","https://openalex.org/W6688386640","https://openalex.org/W6713658392"],"related_works":["https://openalex.org/W2352648934","https://openalex.org/W2378212145","https://openalex.org/W2100968651","https://openalex.org/W2158420043","https://openalex.org/W2071781501","https://openalex.org/W4243803532","https://openalex.org/W1805748654","https://openalex.org/W3193872944","https://openalex.org/W2142248489","https://openalex.org/W1530767163"],"abstract_inverted_index":{"Recently,":[0],"a":[1,20],"lot":[2],"of":[3,27,103,113],"deep":[4,61],"learning":[5,62],"model":[6,79],"successful":[7],"in":[8,13,72,149],"taking":[9],"over":[10],"conventional":[11,33],"methods":[12],"speech":[14,28],"processing":[15],"fields.":[16],"Vector":[17,47,74,136],"quantization":[18,35,58,71,106],"is":[19,37],"popular":[21],"technique":[22],"to":[23,86],"reduce":[24,141],"the":[25,40,46,69,73,92,101,104,109,114,128,134,142],"amount":[26],"data":[29],"before":[30],"transmitting.":[31],"The":[32],"vector":[34,57],"method":[36,107],"based":[38,59],"on":[39,60,82],"mathematical":[41],"model.":[42],"Last":[43],"few":[44,96],"years,":[45],"Quantized":[48,75,137],"Variational":[49,76,138],"AutoEncoder":[50,139],"has":[51],"been":[52],"proposed":[53,105],"for":[54,108],"an":[55],"end-to-end":[56],"techniques.":[63],"In":[64],"this":[65],"paper,":[66],"we":[67],"investigate":[68],"sub-band":[70,135],"AutoEncoder.":[77],"This":[78],"can":[80,140],"concentrate":[81],"specific":[83],"frequency":[84,123],"bands":[85],"assign":[87],"more":[88],"bits":[89],"and":[90],"leave":[91],"unnecessary":[93],"band":[94],"with":[95],"bits.":[97],"Experimental":[98],"results":[99],"show":[100],"efficiency":[102],"spectral":[110],"envelope":[111],"parameters":[112],"high-quality":[115],"vocoder":[116],"that":[117],"operates":[118],"at":[119],"48":[120],"kHz":[121],"sampling":[122],"named":[124],"WORLD":[125],"vocoder.":[126],"At":[127],"same":[129],"four":[130],"target":[131],"bit":[132],"rates,":[133],"Log":[143],"Spectral":[144],"Distortion":[145],"around":[146],"0.93":[147],"dB":[148],"average.":[150]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
