{"id":"https://openalex.org/W2291947139","doi":"https://doi.org/10.1109/apsipa.2015.7415449","title":"Chinese opera genre classification based on multi-feature fusion and extreme learning machine","display_name":"Chinese opera genre classification based on multi-feature fusion and extreme learning machine","publication_year":2015,"publication_date":"2015-12-01","ids":{"openalex":"https://openalex.org/W2291947139","doi":"https://doi.org/10.1109/apsipa.2015.7415449","mag":"2291947139"},"language":"en","primary_location":{"id":"doi:10.1109/apsipa.2015.7415449","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipa.2015.7415449","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","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/A5102003658","display_name":"Jianrong Wang","orcid":"https://orcid.org/0000-0002-8980-1634"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"JianRong Wang","raw_affiliation_strings":["Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073472740","display_name":"Chenliang Wang","orcid":"https://orcid.org/0000-0001-8139-0597"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"ChenLiang Wang","raw_affiliation_strings":["Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027428789","display_name":"Jianguo Wei","orcid":"https://orcid.org/0000-0002-8964-9759"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"JianGuo Wei","raw_affiliation_strings":["Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017251198","display_name":"Jianwu Dang","orcid":"https://orcid.org/0000-0002-9237-4821"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"JianWu Dang","raw_affiliation_strings":["Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"6","issue":null,"first_page":"811","last_page":"814"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9994999766349792,"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":0.9994999766349792,"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/T10788","display_name":"Neuroscience and Music Perception","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13996","display_name":"Diverse Musicological Studies","score":0.9782999753952026,"subfield":{"id":"https://openalex.org/subfields/1210","display_name":"Music"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6859615445137024},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.68301922082901},{"id":"https://openalex.org/keywords/opera","display_name":"Opera","score":0.6761521100997925},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6451222896575928},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5495631694793701},{"id":"https://openalex.org/keywords/majority-rule","display_name":"Majority rule","score":0.5102949142456055},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34444496035575867},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10363075137138367},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.09446719288825989},{"id":"https://openalex.org/keywords/history","display_name":"History","score":0.06493249535560608}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6859615445137024},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.68301922082901},{"id":"https://openalex.org/C530479602","wikidata":"https://www.wikidata.org/wiki/Q1344","display_name":"Opera","level":2,"score":0.6761521100997925},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6451222896575928},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5495631694793701},{"id":"https://openalex.org/C153668964","wikidata":"https://www.wikidata.org/wiki/Q27636","display_name":"Majority rule","level":2,"score":0.5102949142456055},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34444496035575867},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10363075137138367},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.09446719288825989},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.06493249535560608},{"id":"https://openalex.org/C52119013","wikidata":"https://www.wikidata.org/wiki/Q50637","display_name":"Art history","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipa.2015.7415449","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipa.2015.7415449","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W15066456","https://openalex.org/W75825362","https://openalex.org/W585519466","https://openalex.org/W1963631516","https://openalex.org/W1973584296","https://openalex.org/W1993885071","https://openalex.org/W2001709122","https://openalex.org/W2027654323","https://openalex.org/W2053101950","https://openalex.org/W2101294471","https://openalex.org/W2116972814","https://openalex.org/W2125324924","https://openalex.org/W2127432424","https://openalex.org/W2128196382","https://openalex.org/W2131772921","https://openalex.org/W2131913731","https://openalex.org/W2133824856","https://openalex.org/W2134603844","https://openalex.org/W2136951780","https://openalex.org/W2139107403","https://openalex.org/W2139435939","https://openalex.org/W2149970504","https://openalex.org/W2153635508","https://openalex.org/W2167969117","https://openalex.org/W2222577885","https://openalex.org/W2911964244","https://openalex.org/W6603036516","https://openalex.org/W6648981324","https://openalex.org/W6678901678","https://openalex.org/W6679816892"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W2353500159","https://openalex.org/W2386740010","https://openalex.org/W3011881386","https://openalex.org/W635637022","https://openalex.org/W2348070968","https://openalex.org/W2377396514","https://openalex.org/W2969890106","https://openalex.org/W1970869406","https://openalex.org/W2728608649"],"abstract_inverted_index":{"Chinese":[0,8,59,128,143],"traditional":[1,9,60,129,144],"opera":[2,61,130,145],"plays":[3],"an":[4],"important":[5],"role":[6],"in":[7,155],"culture,":[10],"it":[11],"reflects":[12],"the":[13,35,84,97,100],"customs":[14],"and":[15,34,52,80,91],"value":[16],"tendency":[17],"of":[18,99,109,123,142],"different":[19],"areas.":[20],"Though":[21],"researchers":[22],"have":[23],"already":[24],"gained":[25],"some":[26],"achievements,":[27],"studies":[28],"on":[29,49],"this":[30,158],"field":[31],"are":[32,77,106],"scarce":[33],"existing":[36],"achievements":[37],"still":[38],"need":[39],"to":[40,57,82,95],"be":[41],"improved.":[42],"This":[43,116],"paper":[44],"proposes":[45],"a":[46,119],"system":[47,117],"based":[48],"multi-feature":[50,137],"fusion":[51,85,138,148,154],"extreme":[53],"learning":[54],"machine":[55],"(ELM)":[56],"classify":[58],"genre.":[62],"Inspired":[63],"by":[64],"music":[65],"genre":[66,98],"classification,":[67],"each":[68],"aria":[69],"is":[70,149],"split":[71],"into":[72],"multiple":[73],"segments.":[74],"19":[75],"features":[76],"then":[78],"extracted":[79],"fused":[81],"generate":[83],"feature.":[86],"Finally,":[87],"we":[88],"use":[89],"ELM":[90],"majority":[92],"voting":[93],"methods":[94],"determine":[96],"whole":[101],"aria.":[102],"The":[103,132],"research":[104],"data":[105],"800":[107],"arias":[108],"8":[110,126],"typical":[111],"genres":[112],"collected":[113],"from":[114],"Internet.":[115],"achieves":[118],"mean":[120],"classification":[121,140],"accuracy":[122,141],"92%":[124],"among":[125],"famous":[127],"genres.":[131,146],"experimental":[133],"results":[134],"demonstrated":[135],"that":[136],"improves":[139],"Feature":[147],"more":[150],"effective":[151],"than":[152],"decision":[153],"dealing":[156],"with":[157],"problem.":[159]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
