{"id":"https://openalex.org/W2132695714","doi":"https://doi.org/10.1109/icassp.2012.6288822","title":"MFCC enhancement using joint corrupted and noise feature space for highly non-stationary noise environments","display_name":"MFCC enhancement using joint corrupted and noise feature space for highly non-stationary noise environments","publication_year":2012,"publication_date":"2012-03-01","ids":{"openalex":"https://openalex.org/W2132695714","doi":"https://doi.org/10.1109/icassp.2012.6288822","mag":"2132695714"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2012.6288822","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2012.6288822","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5028421346","display_name":"Masayuki Suzuki","orcid":"https://orcid.org/0000-0002-0436-1490"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masayuki Suzuki","raw_affiliation_strings":["University of Tokyo, Japan","The University of Tokyo, JAPAN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"The University of Tokyo, JAPAN","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101618071","display_name":"Takuya Yoshioka","orcid":"https://orcid.org/0009-0003-7791-3545"},"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":"Takuya Yoshioka","raw_affiliation_strings":["NTT Communication Science Laboratories, NTT Corporation, Japan","NTT Communication Science Labs, NTT Corp., Japan#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, NTT Corporation, Japan","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"NTT Communication Science Labs, NTT Corp., Japan#TAB#","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001291873","display_name":"Shinji Watanabe","orcid":"https://orcid.org/0000-0002-5970-8631"},"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":"Shinji Watanabe","raw_affiliation_strings":["NTT Communication Science Laboratories, NTT Corporation, Japan","NTT Communication Science Labs, NTT Corp., Japan#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, NTT Corporation, Japan","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"NTT Communication Science Labs, NTT Corp., Japan#TAB#","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041213266","display_name":"Nobuaki Minematsu","orcid":"https://orcid.org/0000-0002-8778-9555"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Nobuaki Minematsu","raw_affiliation_strings":["University of Tokyo, Japan","The University of Tokyo, JAPAN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"The University of Tokyo, JAPAN","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108197272","display_name":"Keikichi Hirose","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Keikichi Hirose","raw_affiliation_strings":["University of Tokyo, Japan","The University of Tokyo, JAPAN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"The University of Tokyo, JAPAN","institution_ids":["https://openalex.org/I74801974"]}]}],"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":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4109","last_page":"4112"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":1.0,"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":1.0,"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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9957000017166138,"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/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.7121528387069702},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.711370587348938},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6985891461372375},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5973633527755737},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5962380170822144},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5791784524917603},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5316356420516968},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.45479777455329895},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.432275652885437},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42810195684432983},{"id":"https://openalex.org/keywords/value-noise","display_name":"Value noise","score":0.41874009370803833},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.36879634857177734},{"id":"https://openalex.org/keywords/noise-floor","display_name":"Noise floor","score":0.17297405004501343}],"concepts":[{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.7121528387069702},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.711370587348938},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6985891461372375},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5973633527755737},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5962380170822144},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5791784524917603},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5316356420516968},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.45479777455329895},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.432275652885437},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42810195684432983},{"id":"https://openalex.org/C182163834","wikidata":"https://www.wikidata.org/wiki/Q2926529","display_name":"Value noise","level":5,"score":0.41874009370803833},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.36879634857177734},{"id":"https://openalex.org/C187612029","wikidata":"https://www.wikidata.org/wiki/Q17083130","display_name":"Noise floor","level":4,"score":0.17297405004501343},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icassp.2012.6288822","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2012.6288822","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.590.2216","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.590.2216","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.gavo.t.u-tokyo.ac.jp/~mine/paper/PDF/2012/ICASSP_p4109-4112_t2012-3.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W96504731","https://openalex.org/W1140471238","https://openalex.org/W1573570773","https://openalex.org/W2000949213","https://openalex.org/W2106714211","https://openalex.org/W2151484683","https://openalex.org/W2158699620","https://openalex.org/W2165967438","https://openalex.org/W2166090740","https://openalex.org/W6603922934","https://openalex.org/W6634340323"],"related_works":["https://openalex.org/W3044690502","https://openalex.org/W2098539690","https://openalex.org/W3125397569","https://openalex.org/W4287867034","https://openalex.org/W3108403339","https://openalex.org/W2162712524","https://openalex.org/W2116307787","https://openalex.org/W3005783148","https://openalex.org/W3149431777","https://openalex.org/W2116036791"],"abstract_inverted_index":{"One":[0],"of":[1,110,127,146],"the":[2,14,38,46,58,103,111,119,144,147,161],"most":[3],"effective":[4],"approaches":[5],"to":[6,12,56,136,141],"noise":[7,15,39,54,81,130],"robust":[8],"speech":[9],"recognition":[10],"is":[11,26,40,44,133],"remove":[13],"effect":[16],"directly":[17],"from":[18,160],"corrupted":[19,128],"MFCC":[20,32,73],"vectors.":[21],"However,":[22],"VTS":[23,47],"enhancement,":[24,33],"which":[25],"a":[27,52,68,86,90,124],"typical":[28],"method":[29,49,69,96,113,152],"for":[30],"performing":[31],"provides":[34],"limited":[35],"improvement":[36],"when":[37],"highly":[41],"non-stationary.":[42],"This":[43,65],"because":[45],"enhancement":[48],"cannot":[50],"use":[51,137],"time-varying":[53],"model":[55],"keep":[57],"computational":[59,92],"cost":[60],"at":[61,89],"an":[62],"acceptable":[63],"level.":[64],"paper":[66],"proposes":[67],"that":[70,83,116],"can":[71],"enhance":[72],"vectors":[74],"and":[75,129,155,163],"their":[76],"dynamic":[77],"parameters":[78],"by":[79,123],"using":[80],"estimates":[82],"change":[84],"on":[85],"frame-by-frame":[87],"basis":[88],"practical":[91],"cost.":[93],"The":[94,108,150],"proposed":[95,112,135,151],"employs":[97],"stereo":[98],"data-based":[99],"feature":[100],"mapping":[101],"like":[102],"well":[104],"known":[105],"SPLICE":[106,162,166],"algorithm.":[107],"novelty":[109],"lies":[114],"in":[115],"it":[117],"uses":[118],"joint":[120,148],"space":[121],"spanned":[122],"concatenated":[125],"vector":[126],"features.":[131],"It":[132],"also":[134],"linear":[138],"discriminant":[139],"analysis":[140],"effectively":[142],"reduce":[143],"dimensionality":[145],"space.":[149],"achieves":[153],"19.1%":[154],"8.3%":[156],"relative":[157],"error":[158],"reduction":[159],"noise-mean":[164],"normalized":[165],"algorithms,":[167],"respectively.":[168]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}