{"id":"https://openalex.org/W2905992143","doi":"https://doi.org/10.1145/3277453.3277462","title":"An Advance on Gender Classification by Information Preserving Features","display_name":"An Advance on Gender Classification by Information Preserving Features","publication_year":2018,"publication_date":"2018-09-19","ids":{"openalex":"https://openalex.org/W2905992143","doi":"https://doi.org/10.1145/3277453.3277462","mag":"2905992143"},"language":"en","primary_location":{"id":"doi:10.1145/3277453.3277462","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3277453.3277462","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 International Conference on Electronics and Electrical Engineering Technology","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/A5108671758","display_name":"Karthika Kuppusamy","orcid":null},"institutions":[{"id":"https://openalex.org/I111575329","display_name":"Bharathiar University","ror":"https://ror.org/04fht8c22","country_code":"IN","type":"education","lineage":["https://openalex.org/I111575329"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Karthika Kuppusamy","raw_affiliation_strings":["Department of Computer Science, Bharathiar University, Coimbatore"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Bharathiar University, Coimbatore","institution_ids":["https://openalex.org/I111575329"]}]},{"author_position":"last","author":{"id":null,"display_name":"Chandra Eswaran","orcid":null},"institutions":[{"id":"https://openalex.org/I111575329","display_name":"Bharathiar University","ror":"https://ror.org/04fht8c22","country_code":"IN","type":"education","lineage":["https://openalex.org/I111575329"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chandra Eswaran","raw_affiliation_strings":["Department of Computer Science, Bharathiar University, Coimbatore"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Bharathiar University, Coimbatore","institution_ids":["https://openalex.org/I111575329"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5108671758"],"corresponding_institution_ids":["https://openalex.org/I111575329"],"apc_list":null,"apc_paid":null,"fwci":0.4975,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.66116132,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"227","last_page":"231"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9987999796867371,"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":0.9987999796867371,"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":0.9987000226974487,"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":0.9904999732971191,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7387183904647827},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7363720536231995},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6783962249755859},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6392886638641357},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6012131571769714},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5780287981033325},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48369652032852173},{"id":"https://openalex.org/keywords/loudness","display_name":"Loudness","score":0.45692160725593567},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.4476637840270996},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4200073182582855},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4181543290615082}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7387183904647827},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7363720536231995},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6783962249755859},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6392886638641357},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6012131571769714},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5780287981033325},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48369652032852173},{"id":"https://openalex.org/C79018884","wikidata":"https://www.wikidata.org/wiki/Q622324","display_name":"Loudness","level":2,"score":0.45692160725593567},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.4476637840270996},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4200073182582855},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4181543290615082},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"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/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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3277453.3277462","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3277453.3277462","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 International Conference on Electronics and Electrical Engineering Technology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.44999998807907104},{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":0.4000000059604645}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W33658992","https://openalex.org/W247701703","https://openalex.org/W759337373","https://openalex.org/W1981803332","https://openalex.org/W1996601463","https://openalex.org/W2028124797","https://openalex.org/W2030459594","https://openalex.org/W2057451158","https://openalex.org/W2068634874","https://openalex.org/W2111047211","https://openalex.org/W2112465581","https://openalex.org/W2185383584","https://openalex.org/W2326623785","https://openalex.org/W2412333281","https://openalex.org/W2492342376","https://openalex.org/W4210327135"],"related_works":["https://openalex.org/W2048014685","https://openalex.org/W3009295899","https://openalex.org/W2370972896","https://openalex.org/W4317383455","https://openalex.org/W2548511587","https://openalex.org/W4293232884","https://openalex.org/W2422472940","https://openalex.org/W2019475500","https://openalex.org/W2548162870","https://openalex.org/W2144773493"],"abstract_inverted_index":{"One":[0],"of":[1,50,114,140,150,178,206],"the":[2,17,47,55,70,82,94,136,141,148,163,200,204,212,216,228,234],"most":[3],"challenging":[4],"issues":[5],"in":[6,86,244],"Speaker's":[7],"Gender":[8],"Classification":[9],"(SGC)":[10],"is":[11,90,209],"feature":[12],"extraction.":[13],"Since,":[14],"it":[15],"degrades":[16],"classification":[18,56,83,202,220,225,232,235],"accuracy":[19,72,221,236],"due":[20],"to":[21,53,80,199],"information":[22,61],"loss":[23],"during":[24],"features":[25,49,77,107,113,166,171],"extraction":[26],"using":[27,41,131,175,223],"speech":[28,51,115],"signals.":[29],"In":[30,102,119],"previous":[31],"researches,":[32],"Perceptual":[33,123],"Linear":[34,124],"Prediction":[35,125],"(PLP)":[36],"coefficients":[37,96,127],"were":[38,78],"extracted":[39,130,164],"by":[40,92,174,222],"Blackman":[42],"windowing":[43,100,133],"method":[44],"along":[45],"with":[46,169],"other":[48],"signal":[52,116],"improve":[54,81],"accuracy.":[57],"However,":[58],"still":[59],"some":[60],"was":[62],"lost":[63],"at":[64,193],"those":[65],"window":[66],"edges":[67],"which":[68],"degrade":[69],"recognition":[71],"and":[73,111,157,172,189,242,247],"also":[74,129],"more":[75],"efficient":[76],"required":[79],"performance.":[84],"Hence":[85],"this":[87,103],"paper,":[88],"SGC":[89],"improved":[91],"extracting":[93],"PLP":[95],"based":[97,146],"on":[98,147,219],"novel":[99],"technique.":[101,226],"technique,":[104,233],"initially":[105],"type-1":[106,170],"such":[108],"as":[109],"spectral":[110],"prosodic":[112],"are":[117,128,144,167,238],"extracted.":[118],"addition,":[120],"Information":[121],"Preserving":[122],"(IPPLP)":[126],"Slepian":[132],"method.":[134],"Moreover,":[135],"frequency-dependent":[137],"transmission":[138],"characteristics":[139],"outer":[142],"ear":[143],"compensated":[145],"analysis":[149],"time-varying":[151],"Equal":[152],"Loudness":[153],"Contour":[154],"(ELC)":[155],"curves":[156],"Peak-to-Loudness":[158],"Ratio":[159],"(PLR).":[160],"After":[161],"that,":[162],"IPPLP":[165],"fused":[168],"classified":[173],"different":[176],"combinations":[177],"classifiers":[179],"like":[180],"Gaussian":[181],"Mixture":[182],"Model":[183],"(GMM),":[184],"Support":[185],"Vector":[186],"Machine":[187],"(SVM)":[188],"GMM":[190],"supervectors-based":[191],"SVM":[192,246],"score":[194],"level":[195],"fusion":[196],"scheme.":[197],"According":[198],"final":[201],"result,":[203],"type":[205],"speaker's":[207,230],"gender":[208,231],"recognized.":[210],"Finally,":[211],"experimental":[213],"results":[214],"show":[215],"significant":[217],"improvements":[218],"proposed":[224,229],"With":[227],"values":[237],"obtained":[239],"38.55%,":[240],"62.65%":[241],"69.88%":[243],"GMM,":[245],"GMM-SVM":[248],"classification,":[249],"respectively.":[250]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}