{"id":"https://openalex.org/W4200219451","doi":"https://doi.org/10.5753/eniac.2021.18297","title":"A Comparison of Deep Learning Architectures for Automatic Gender Recognition from Audio Signals","display_name":"A Comparison of Deep Learning Architectures for Automatic Gender Recognition from Audio Signals","publication_year":2021,"publication_date":"2021-11-29","ids":{"openalex":"https://openalex.org/W4200219451","doi":"https://doi.org/10.5753/eniac.2021.18297"},"language":"pt","primary_location":{"id":"doi:10.5753/eniac.2021.18297","doi":"https://doi.org/10.5753/eniac.2021.18297","is_oa":true,"landing_page_url":"https://doi.org/10.5753/eniac.2021.18297","pdf_url":"https://sol.sbc.org.br/index.php/eniac/article/download/18297/18131","source":null,"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Anais do XVIII Encontro Nacional de Intelig\u00eancia Artificial e Computacional (ENIAC 2021)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://sol.sbc.org.br/index.php/eniac/article/download/18297/18131","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018617522","display_name":"Alef Iury Siqueira Ferreira","orcid":"https://orcid.org/0000-0002-9119-6357","observed_orcids":["https://orcid.org/0000-0002-9119-6357"]},"institutions":[{"id":"https://openalex.org/I68106152","display_name":"Universidade Federal de Goi\u00e1s","ror":"https://ror.org/0039d5757","country_code":"BR","type":"education","lineage":["https://openalex.org/I68106152"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Alef Iury S. Ferreira","raw_affiliation_strings":["UFG","Instituto de Inform\u00e1tica -Universidade Federal de Goi\u00e1s (UFG) Goi\u00e2nia -Goi\u00e1s -Brasil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UFG","institution_ids":["https://openalex.org/I68106152"]},{"raw_affiliation_string":"Instituto de Inform\u00e1tica -Universidade Federal de Goi\u00e1s (UFG) Goi\u00e2nia -Goi\u00e1s -Brasil","institution_ids":["https://openalex.org/I68106152"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057639095","display_name":"Frederico Santos de Oliveira","orcid":"https://orcid.org/0000-0002-5885-6747","observed_orcids":["https://orcid.org/0000-0002-5885-6747"]},"institutions":[{"id":"https://openalex.org/I32725510","display_name":"Universidade Federal de Mato Grosso","ror":"https://ror.org/01mqvjv41","country_code":"BR","type":"education","lineage":["https://openalex.org/I32725510"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Frederico S. Oliveira","raw_affiliation_strings":["UFMT","C\u00e2mpus V\u00e1rzea Grande -Universidade Federal de Mato Grosso (UFMT) Cuiab\u00e1 -Mato Grosso -Brasil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UFMT","institution_ids":["https://openalex.org/I32725510"]},{"raw_affiliation_string":"C\u00e2mpus V\u00e1rzea Grande -Universidade Federal de Mato Grosso (UFMT) Cuiab\u00e1 -Mato Grosso -Brasil","institution_ids":["https://openalex.org/I32725510"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017215877","display_name":"N\u00e1dia F. Felipe da Silva","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I68106152","display_name":"Universidade Federal de Goi\u00e1s","ror":"https://ror.org/0039d5757","country_code":"BR","type":"education","lineage":["https://openalex.org/I68106152"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"N\u00e1dia F. Felipe da Silva","raw_affiliation_strings":["UFG","Instituto de Inform\u00e1tica -Universidade Federal de Goi\u00e1s (UFG) Goi\u00e2nia -Goi\u00e1s -Brasil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UFG","institution_ids":["https://openalex.org/I68106152"]},{"raw_affiliation_string":"Instituto de Inform\u00e1tica -Universidade Federal de Goi\u00e1s (UFG) Goi\u00e2nia -Goi\u00e1s -Brasil","institution_ids":["https://openalex.org/I68106152"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024062827","display_name":"Anderson S. Soares","orcid":"https://orcid.org/0000-0001-9701-0819","observed_orcids":["https://orcid.org/0000-0001-9701-0819"]},"institutions":[{"id":"https://openalex.org/I68106152","display_name":"Universidade Federal de Goi\u00e1s","ror":"https://ror.org/0039d5757","country_code":"BR","type":"education","lineage":["https://openalex.org/I68106152"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Anderson S. Soares","raw_affiliation_strings":["UFG","Instituto de Inform\u00e1tica -Universidade Federal de Goi\u00e1s (UFG) Goi\u00e2nia -Goi\u00e1s -Brasil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UFG","institution_ids":["https://openalex.org/I68106152"]},{"raw_affiliation_string":"Instituto de Inform\u00e1tica -Universidade Federal de Goi\u00e1s (UFG) Goi\u00e2nia -Goi\u00e1s -Brasil","institution_ids":["https://openalex.org/I68106152"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14718427,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"715","last_page":"726"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9937000274658203,"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":0.9937000274658203,"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":0.002099999925121665,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.0017999999690800905,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/speech-recognition","display_name":"speech recognition","score":0.5299999713897705},{"id":"https://openalex.org/keywords/deep-learning-architectures","display_name":"deep learning architectures","score":0.7900000214576721},{"id":"https://openalex.org/keywords/1d-convolutional-neural-network","display_name":"1D convolutional neural network","score":0.8619999885559082},{"id":"https://openalex.org/keywords/fully-connected-neural-network","display_name":"fully connected neural network","score":0.9520000219345093},{"id":"https://openalex.org/keywords/computational-cost","display_name":"computational cost","score":0.6610000133514404},{"id":"https://openalex.org/keywords/memory-usage","display_name":"memory usage","score":0.7350000143051147},{"id":"https://openalex.org/keywords/training-duration","display_name":"training duration","score":0.8840000033378601},{"id":"https://openalex.org/keywords/speech-analysis","display_name":"speech analysis","score":0.421999990940094}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6229273676872253},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.51896733045578},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42583000659942627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4073122441768646},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40472501516342163},{"id":"https://openalex.org/C15708023","wikidata":"https://www.wikidata.org/wiki/Q80083","display_name":"Humanities","level":1,"score":0.3232409656047821},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.1915929615497589}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5753/eniac.2021.18297","doi":"https://doi.org/10.5753/eniac.2021.18297","is_oa":true,"landing_page_url":"https://doi.org/10.5753/eniac.2021.18297","pdf_url":"https://sol.sbc.org.br/index.php/eniac/article/download/18297/18131","source":null,"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Anais do XVIII Encontro Nacional de Intelig\u00eancia Artificial e Computacional (ENIAC 2021)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.5753/eniac.2021.18297","doi":"https://doi.org/10.5753/eniac.2021.18297","is_oa":true,"landing_page_url":"https://doi.org/10.5753/eniac.2021.18297","pdf_url":"https://sol.sbc.org.br/index.php/eniac/article/download/18297/18131","source":null,"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Anais do XVIII Encontro Nacional de Intelig\u00eancia Artificial e Computacional (ENIAC 2021)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6025999784469604,"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality"},{"score":0.5676000118255615,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4200219451.pdf","grobid_xml":"https://content.openalex.org/works/W4200219451.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W173856587","https://openalex.org/W1494198834","https://openalex.org/W1503398984","https://openalex.org/W1975929202","https://openalex.org/W2091425152","https://openalex.org/W2097998348","https://openalex.org/W2121551440","https://openalex.org/W2125324924","https://openalex.org/W2129033354","https://openalex.org/W2129456397","https://openalex.org/W2134389297","https://openalex.org/W2191779130","https://openalex.org/W2492342376","https://openalex.org/W2767286248","https://openalex.org/W2887949187","https://openalex.org/W2931364255","https://openalex.org/W2939710050","https://openalex.org/W2972753962","https://openalex.org/W2976594877","https://openalex.org/W3006217732","https://openalex.org/W3040791725","https://openalex.org/W3102535553","https://openalex.org/W3149444771","https://openalex.org/W3216401400"],"related_works":["https://openalex.org/W4312983853","https://openalex.org/W4410542063","https://openalex.org/W7208831151","https://openalex.org/W4404645514","https://openalex.org/W3109465620","https://openalex.org/W2965655638","https://openalex.org/W2589181618","https://openalex.org/W4409475261","https://openalex.org/W3124809995","https://openalex.org/W4388682772"],"abstract_inverted_index":{"O":[0],"reconhecimento":[1,69],"de":[2,14,28,39,61,64,70,105,110],"g\u00eanero":[3,71],"a":[4,24,57,72],"partir":[5,73],"da":[6,35,74],"fala":[7,15],"\u00e9":[8,47],"um":[9],"problema":[10,46],"relacionado":[11],"\u00e0":[12,30],"an\u00e1lise":[13],"humana,":[16],"e":[17,37,55,59],"possui":[18],"diversas":[19],"aplica\u00e7\u00f5es":[20],"que":[21,42,79,91],"v\u00e3o":[22],"desde":[23],"personaliza\u00e7\u00e3o":[25],"na":[26],"recomenda\u00e7\u00e3o":[27],"produtos":[29],"ci\u00eancia":[31],"forense.":[32],"A":[33],"identifica\u00e7\u00e3o":[34],"efici\u00eancia":[36,58],"custos":[38,60],"diferentes":[40,62],"abordagens":[41],"lidam":[43],"com":[44,99],"esse":[45],"imprescind\u00edvel.":[48],"Este":[49],"trabalho":[50],"tem":[51],"como":[52],"foco":[53],"investigar":[54],"comparar":[56],"arquiteturas":[63],"deep":[65],"learning":[66],"para":[67],"o":[68,80,92],"fala.":[75],"Os":[76],"resultados":[77,97],"mostram":[78],"modelo":[81,93],"convolucional":[82],"unidimensional":[83],"consegue":[84],"os":[85],"melhores":[86],"resultados.":[87],"No":[88],"entanto,":[89],"constatou-se":[90],"fully":[94],"connected":[95],"apresentou":[96],"pr\u00f3ximos":[98],"menor":[100],"custo,":[101],"tanto":[102],"no":[103,108],"uso":[104],"mem\u00f3ria,":[106],"quanto":[107],"tempo":[109],"treinamento.":[111]},"counts_by_year":[],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2021-12-31T00:00:00"}
