{"id":"https://openalex.org/W2090711167","doi":"https://doi.org/10.1109/icdsp.2014.6900767","title":"Improved Voice Activity Detection based on support vector machine with high separable speech feature vectors","display_name":"Improved Voice Activity Detection based on support vector machine with high separable speech feature vectors","publication_year":2014,"publication_date":"2014-08-01","ids":{"openalex":"https://openalex.org/W2090711167","doi":"https://doi.org/10.1109/icdsp.2014.6900767","mag":"2090711167"},"language":"en","primary_location":{"id":"doi:10.1109/icdsp.2014.6900767","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2014.6900767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 19th International Conference on Digital Signal Processing","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/A5002795838","display_name":"Yuexian Zou","orcid":"https://orcid.org/0000-0001-9999-6140"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Y. X. Zou","raw_affiliation_strings":["ADSPLAB, Peking University, Shenzhen, China","ADSPLAB/ELIP, School of Electronic Computer, Engineering, Peking University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"ADSPLAB, Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"ADSPLAB/ELIP, School of Electronic Computer, Engineering, Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114109290","display_name":"W. Q. Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"W. Q. Zheng","raw_affiliation_strings":["ADSPLAB, Peking University, Shenzhen, China","ADSPLAB/ELIP, School of Electronic Computer, Engineering, Peking University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"ADSPLAB, Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"ADSPLAB/ELIP, School of Electronic Computer, Engineering, Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032675026","display_name":"Wei Shi","orcid":"https://orcid.org/0000-0002-3129-0296"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Shi","raw_affiliation_strings":["ADSPLAB, Peking University, Shenzhen, China","ADSPLAB/ELIP, School of Electronic Computer, Engineering, Peking University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"ADSPLAB, Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"ADSPLAB/ELIP, School of Electronic Computer, Engineering, Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100410286","display_name":"Hong Liu","orcid":"https://orcid.org/0000-0002-0896-8409"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Liu","raw_affiliation_strings":["ADSPLAB, Peking University, Shenzhen, China","ADSPLAB/ELIP, School of Electronic Computer, Engineering, Peking University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"ADSPLAB, Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"ADSPLAB/ELIP, School of Electronic Computer, Engineering, Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5002795838"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":3.2024,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.92702546,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"763","last_page":"767"},"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9987999796867371,"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":0.9976000189781189,"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/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.8734710812568665},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.7644045352935791},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7103232741355896},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.614540159702301},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.6129064559936523},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5962949395179749},{"id":"https://openalex.org/keywords/linear-predictive-coding","display_name":"Linear predictive coding","score":0.5740746259689331},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.565850555896759},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5038010478019714},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4944748878479004},{"id":"https://openalex.org/keywords/voice-activity-detection","display_name":"Voice activity detection","score":0.4931301772594452},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4481576085090637},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.43983522057533264},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.42593446373939514},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41839587688446045},{"id":"https://openalex.org/keywords/speech-processing","display_name":"Speech processing","score":0.3860064446926117}],"concepts":[{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.8734710812568665},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7644045352935791},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7103232741355896},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.614540159702301},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.6129064559936523},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5962949395179749},{"id":"https://openalex.org/C59883199","wikidata":"https://www.wikidata.org/wiki/Q1826438","display_name":"Linear predictive coding","level":3,"score":0.5740746259689331},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.565850555896759},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5038010478019714},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4944748878479004},{"id":"https://openalex.org/C204201278","wikidata":"https://www.wikidata.org/wiki/Q1332614","display_name":"Voice activity detection","level":3,"score":0.4931301772594452},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4481576085090637},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.43983522057533264},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42593446373939514},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41839587688446045},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.3860064446926117},{"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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdsp.2014.6900767","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2014.6900767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 19th International Conference on Digital Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.7200000286102295,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1637570796","https://openalex.org/W1727733255","https://openalex.org/W1922223312","https://openalex.org/W1972723604","https://openalex.org/W1980862284","https://openalex.org/W2047365428","https://openalex.org/W2063541597","https://openalex.org/W2108819501","https://openalex.org/W2110622865","https://openalex.org/W2115717467","https://openalex.org/W2141102245","https://openalex.org/W2145030715","https://openalex.org/W2148154194","https://openalex.org/W2149053750","https://openalex.org/W2152395643","https://openalex.org/W2156798212","https://openalex.org/W2157363431","https://openalex.org/W2167516348","https://openalex.org/W2171875099","https://openalex.org/W2399836148"],"related_works":["https://openalex.org/W2018086531","https://openalex.org/W1980297060","https://openalex.org/W2387604097","https://openalex.org/W4385672897","https://openalex.org/W2373675101","https://openalex.org/W1523214805","https://openalex.org/W4294771049","https://openalex.org/W2559040841","https://openalex.org/W2341426843","https://openalex.org/W2131711534"],"abstract_inverted_index":{"Voice":[0],"Activity":[1],"Detection":[2],"(VAD)":[3],"is":[4,35,65,75,97,103,252],"one":[5],"of":[6,59,121,145,174,201,207],"the":[7,31,39,51,55,62,73,79,122,127,135,143,146,170,175,193,197,215,219,226,240,243,246,262,267],"key":[8],"techniques":[9],"for":[10,106,150,256],"many":[11],"speech":[12,40,80,83,152,158,202],"applications.":[13],"Existing":[14],"VAD":[15,52,63,74,123,236,276],"algorithms":[16],"have":[17,160],"shown":[18],"unsatisfied":[19],"performance":[20],"under":[21],"nonstationary":[22],"noise":[23,227],"and":[24,41,84,119,153,182,203,242,278],"low":[25,45],"Signal-to-Noise-Ratio":[26],"(SNR)":[27],"situations.":[28],"Motivated":[29],"by":[30,77,133,238],"fact":[32],"that":[33,192,266],"people":[34],"able":[36],"to":[37,125,141,225],"distinguish":[38],"non-speech":[42,85,154],"even":[43],"in":[44,209,283],"SNR":[46],"situations,":[47],"this":[48],"paper":[49],"studies":[50],"technique":[53,124],"from":[54],"pattern":[56],"recognition":[57],"point":[58],"view,":[60],"where":[61],"essentially":[64],"formulated":[66],"as":[67,245,254],"a":[68,233,235],"binary":[69,107],"classification":[70,108,208],"problem.":[71],"Specifically,":[72],"implemented":[76],"classifying":[78,151],"signal":[81],"into":[82,163],"segments.":[86,155],"The":[87,258],"radial":[88],"basis":[89],"function":[90],"(RBF)":[91],"based":[92],"support":[93],"vector":[94],"machine":[95],"(SVM)":[96],"employed":[98],"with":[99,110,228,261],"supervised":[100],"manner,":[101],"which":[102,251],"perfectly":[104],"suitable":[105],"tasks":[109],"some":[111],"training":[112],"samples.":[113],"Aiming":[114],"at":[115,287],"achieving":[116],"improved":[117],"accuracy":[118],"robustness":[120],"noise,":[126],"feature":[128,147],"selection":[129],"has":[130,248,271],"been":[131,161,249],"conducted":[132],"introducing":[134],"class":[136],"separation":[137],"measure":[138],"(CSM)":[139],"criterion":[140],"evaluate":[142],"capability":[144],"vectors":[148],"extracted":[149],"Most":[156],"famous":[157],"features":[159,195,221],"taken":[162],"account,":[164],"including":[165],"Mel-frequency":[166],"cepstral":[167,185],"coefficients":[168],"(MFCC),":[169],"principal":[171],"component":[172],"analysis":[173],"MFCC":[176,194],"(PCA-MFCC),":[177],"linear":[178,183],"predictive":[179,184],"coding":[180,186],"(LPC)":[181],"(LPCC).":[187],"Intensive":[188],"experimental":[189,259],"results":[190,260],"show":[191,265],"capture":[196],"most":[198],"relevant":[199],"information":[200],"keep":[204],"good":[205],"separability":[206],"different":[210],"noisy":[211,285],"conditions,":[212],"so":[213],"do":[214],"PCA-MFCC":[216,220,241],"features.":[217],"Moreover,":[218],"are":[222],"more":[223,281],"robust":[224,282],"less":[229],"computational":[230],"cost.":[231],"As":[232],"result,":[234],"method":[237,270],"using":[239],"RBF-SVM":[244],"classifier":[247],"developed,":[250],"termed":[253],"PCA-SVM-VAD":[255,269],"short.":[257],"NOIZEUS":[263],"database":[264],"proposed":[268],"clear":[272],"improvements":[273],"over":[274],"other":[275],"methods":[277],"performs":[279],"much":[280],"car":[284],"environment":[286],"various":[288],"SNRs.":[289]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":2}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}