{"id":"https://openalex.org/W4320024018","doi":"https://doi.org/10.1109/bigdata55660.2022.10020353","title":"Classifying Perceived Emotions based on Polarity of Arousal and Valence from Sound Events","display_name":"Classifying Perceived Emotions based on Polarity of Arousal and Valence from Sound Events","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4320024018","doi":"https://doi.org/10.1109/bigdata55660.2022.10020353"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020353","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020353","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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/A5048882270","display_name":"Pooja Krishan","orcid":null},"institutions":[{"id":"https://openalex.org/I51504820","display_name":"San Jose State University","ror":"https://ror.org/04qyvz380","country_code":"US","type":"education","lineage":["https://openalex.org/I51504820"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Pooja Krishan","raw_affiliation_strings":["San Jose State University,Department of Computer Science","Department of Computer Science, San Jose State University"],"affiliations":[{"raw_affiliation_string":"San Jose State University,Department of Computer Science","institution_ids":["https://openalex.org/I51504820"]},{"raw_affiliation_string":"Department of Computer Science, San Jose State University","institution_ids":["https://openalex.org/I51504820"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083906665","display_name":"Faranak Abri","orcid":"https://orcid.org/0000-0003-3028-094X"},"institutions":[{"id":"https://openalex.org/I51504820","display_name":"San Jose State University","ror":"https://ror.org/04qyvz380","country_code":"US","type":"education","lineage":["https://openalex.org/I51504820"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Faranak Abri","raw_affiliation_strings":["San Jose State University,Department of Computer Science","Department of Computer Science, San Jose State University"],"affiliations":[{"raw_affiliation_string":"San Jose State University,Department of Computer Science","institution_ids":["https://openalex.org/I51504820"]},{"raw_affiliation_string":"Department of Computer Science, San Jose State University","institution_ids":["https://openalex.org/I51504820"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5048882270"],"corresponding_institution_ids":["https://openalex.org/I51504820"],"apc_list":null,"apc_paid":null,"fwci":0.4157,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60869565,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2849","last_page":"2856"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9984999895095825,"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"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9958000183105469,"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/T12032","display_name":"Multisensory perception and integration","score":0.9861000180244446,"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/feature-selection","display_name":"Feature selection","score":0.7015154361724854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.670831561088562},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6633650660514832},{"id":"https://openalex.org/keywords/valence","display_name":"Valence (chemistry)","score":0.6480614542961121},{"id":"https://openalex.org/keywords/arousal","display_name":"Arousal","score":0.6424169540405273},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.628348171710968},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6280730962753296},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5564035773277283},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4631271958351135},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4519892632961273},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4507206082344055},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.444486141204834},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4382418394088745},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1724403202533722}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7015154361724854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.670831561088562},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6633650660514832},{"id":"https://openalex.org/C168900304","wikidata":"https://www.wikidata.org/wiki/Q171407","display_name":"Valence (chemistry)","level":2,"score":0.6480614542961121},{"id":"https://openalex.org/C36951298","wikidata":"https://www.wikidata.org/wiki/Q379784","display_name":"Arousal","level":2,"score":0.6424169540405273},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.628348171710968},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6280730962753296},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5564035773277283},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4631271958351135},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4519892632961273},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4507206082344055},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.444486141204834},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4382418394088745},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1724403202533722},{"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},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020353","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020353","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1582591009","https://openalex.org/W1668904664","https://openalex.org/W2064149108","https://openalex.org/W2084326765","https://openalex.org/W2095436958","https://openalex.org/W2102077450","https://openalex.org/W2165857685","https://openalex.org/W2294467024","https://openalex.org/W2408751413","https://openalex.org/W2786498952","https://openalex.org/W2922915988","https://openalex.org/W2985912102","https://openalex.org/W2992634693","https://openalex.org/W3026744663","https://openalex.org/W3036601975","https://openalex.org/W3089423349","https://openalex.org/W3094550259","https://openalex.org/W3096282833","https://openalex.org/W3138139790","https://openalex.org/W3206536277","https://openalex.org/W4200174246","https://openalex.org/W6771438384","https://openalex.org/W6777591162","https://openalex.org/W6780218876","https://openalex.org/W6781033888"],"related_works":["https://openalex.org/W2029072726","https://openalex.org/W91913183","https://openalex.org/W2936882366","https://openalex.org/W1987182177","https://openalex.org/W2073883415","https://openalex.org/W2736893848","https://openalex.org/W2128698257","https://openalex.org/W1544055438","https://openalex.org/W3003450285","https://openalex.org/W2085024878"],"abstract_inverted_index":{"Sonification":[0],"uses":[1],"sounds":[2],"to":[3,92],"glean":[4],"insights":[5],"about":[6],"information":[7],"and":[8,25,41,59,72,76,79,95,118,136],"activities":[9],"in":[10,67],"a":[11,130],"person\u2019s":[12],"life.":[13],"There":[14],"are":[15,63,82],"two":[16,37],"types":[17],"of":[18,98,134],"emotions":[19,24,34],"based":[20,35],"on":[21,31,36,101,120],"sounds:":[22],"perceived":[23,33],"induced":[26],"emotions.":[27],"This":[28,84],"paper":[29,85],"focuses":[30],"classifying":[32],"dimensions":[38],"\u2013":[39],"arousal":[40],"valence,":[42],"using":[43,74,108,125],"several":[44],"deep-learning":[45],"models.":[46],"Four":[47],"feature":[48],"selection":[49],"techniques,":[50,78],"Forward":[51],"Feature":[52,55],"Selection,":[53],"Recursive":[54],"Elimination,":[56],"Random":[57],"Forest,":[58],"Principal":[60],"Component":[61],"Analysis,":[62],"performed;":[64],"class":[65],"imbalance":[66],"the":[68,80,87,96,102,121,127,138,147],"dataset":[69,104,124],"is":[70,106,137],"demonstrated":[71],"handled":[73],"under-sampling,":[75],"over-sampling":[77],"results":[81],"compared.":[83],"shows":[86],"need":[88],"for":[89],"balanced":[90,103,123],"data":[91],"train":[93],"classifiers":[94,100],"advantages":[97],"running":[99],"that":[105,141],"generated":[107],"sampling":[109],"techniques.":[110],"The":[111],"eXtreme":[112],"Gradient":[113],"Boosting":[114],"(XgB)":[115],"classifier":[116],"trained":[117],"tested":[119],"over-sampled":[122],"all":[126,146],"features":[128],"generates":[129],"test":[131],"F1":[132],"score":[133],"81.5":[135],"best":[139],"model":[140],"can":[142],"be":[143],"selected":[144],"from":[145],"classifiers.":[148]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
