{"id":"https://openalex.org/W2944458161","doi":"https://doi.org/10.3390/e21050479","title":"3D CNN-Based Speech Emotion Recognition Using K-Means Clustering and Spectrograms","display_name":"3D CNN-Based Speech Emotion Recognition Using K-Means Clustering and Spectrograms","publication_year":2019,"publication_date":"2019-05-08","ids":{"openalex":"https://openalex.org/W2944458161","doi":"https://doi.org/10.3390/e21050479","mag":"2944458161","pmid":"https://pubmed.ncbi.nlm.nih.gov/33267193"},"language":"en","primary_location":{"id":"doi:10.3390/e21050479","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e21050479","pdf_url":"https://www.mdpi.com/1099-4300/21/5/479/pdf?version=1557920600","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/21/5/479/pdf?version=1557920600","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091640248","display_name":"Noushin Hajarolasvadi","orcid":"https://orcid.org/0000-0002-3120-5370"},"institutions":[{"id":"https://openalex.org/I36515993","display_name":"Eastern Mediterranean University","ror":"https://ror.org/00excyz84","country_code":"CY","type":"education","lineage":["https://openalex.org/I36515993"]}],"countries":["CY"],"is_corresponding":true,"raw_author_name":"Noushin Hajarolasvadi","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, Eastern Mediterranean University, 99628 Gazimagusa, North Cyprus, via Mersin 10, Turkey"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Eastern Mediterranean University, 99628 Gazimagusa, North Cyprus, via Mersin 10, Turkey","institution_ids":["https://openalex.org/I36515993"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024772165","display_name":"Hasan Demirel","orcid":"https://orcid.org/0000-0001-5035-9634"},"institutions":[{"id":"https://openalex.org/I36515993","display_name":"Eastern Mediterranean University","ror":"https://ror.org/00excyz84","country_code":"CY","type":"education","lineage":["https://openalex.org/I36515993"]}],"countries":["CY"],"is_corresponding":false,"raw_author_name":"Hasan Demirel","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, Eastern Mediterranean University, 99628 Gazimagusa, North Cyprus, via Mersin 10, Turkey"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Eastern Mediterranean University, 99628 Gazimagusa, North Cyprus, via Mersin 10, Turkey","institution_ids":["https://openalex.org/I36515993"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5091640248"],"corresponding_institution_ids":["https://openalex.org/I36515993"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":16.3484,"has_fulltext":false,"cited_by_count":153,"citation_normalized_percentile":{"value":0.99411062,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"21","issue":"5","first_page":"479","last_page":"479"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9998000264167786,"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":0.9998000264167786,"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/T10860","display_name":"Speech and Audio Processing","score":0.9973000288009644,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/spectrogram","display_name":"Spectrogram","score":0.8992021083831787},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7771168947219849},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6842504143714905},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6627649068832397},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5938296318054199},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5916882157325745},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.5912139415740967},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5789783000946045},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5396767258644104},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4562356472015381},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44109833240509033}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.8992021083831787},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7771168947219849},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6842504143714905},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6627649068832397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5938296318054199},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5916882157325745},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.5912139415740967},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5789783000946045},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5396767258644104},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4562356472015381},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44109833240509033},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/e21050479","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e21050479","pdf_url":"https://www.mdpi.com/1099-4300/21/5/479/pdf?version=1557920600","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:33267193","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33267193","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:322022d295fb41298d5da9304dba9bc5","is_oa":true,"landing_page_url":"https://doaj.org/article/322022d295fb41298d5da9304dba9bc5","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 21, Iss 5, p 479 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/21/5/479/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/e21050479","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:7514968","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7514968","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e21050479","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e21050479","pdf_url":"https://www.mdpi.com/1099-4300/21/5/479/pdf?version=1557920600","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6600000262260437,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2944458161.pdf","grobid_xml":"https://content.openalex.org/works/W2944458161.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W84641558","https://openalex.org/W175750906","https://openalex.org/W216108723","https://openalex.org/W1522734439","https://openalex.org/W1666984270","https://openalex.org/W1686810756","https://openalex.org/W1995562189","https://openalex.org/W2009059481","https://openalex.org/W2026410165","https://openalex.org/W2087618018","https://openalex.org/W2090431713","https://openalex.org/W2096733369","https://openalex.org/W2106433180","https://openalex.org/W2117539524","https://openalex.org/W2132037657","https://openalex.org/W2143317947","https://openalex.org/W2146334809","https://openalex.org/W2148154194","https://openalex.org/W2150593711","https://openalex.org/W2157297238","https://openalex.org/W2163605009","https://openalex.org/W2164471543","https://openalex.org/W2164699598","https://openalex.org/W2168692779","https://openalex.org/W2406222150","https://openalex.org/W2545200558","https://openalex.org/W2587299955","https://openalex.org/W2598207902","https://openalex.org/W2624340939","https://openalex.org/W2725010525","https://openalex.org/W2734984521","https://openalex.org/W2746521834","https://openalex.org/W2747664154","https://openalex.org/W2766756589","https://openalex.org/W2800170478","https://openalex.org/W2806051338","https://openalex.org/W2884739346","https://openalex.org/W2889191349","https://openalex.org/W2949117887","https://openalex.org/W2962963658","https://openalex.org/W2964054038","https://openalex.org/W2964113820","https://openalex.org/W2964121744","https://openalex.org/W3013879769","https://openalex.org/W3099206234","https://openalex.org/W3104696513","https://openalex.org/W3141819983","https://openalex.org/W4249723087"],"related_works":["https://openalex.org/W2530685530","https://openalex.org/W4375868962","https://openalex.org/W2011227383","https://openalex.org/W4317383455","https://openalex.org/W2548511587","https://openalex.org/W2422472940","https://openalex.org/W2019475500","https://openalex.org/W2548162870","https://openalex.org/W2095030957","https://openalex.org/W2066827917"],"abstract_inverted_index":{"Detecting":[0],"human":[1],"intentions":[2],"and":[3,63,134,154,173],"emotions":[4],"helps":[5],"improve":[6],"human-robot":[7],"interactions.":[8],"Emotion":[9,167],"recognition":[10,26],"has":[11,150],"been":[12],"a":[13,125,136,142],"challenging":[14],"research":[15],"direction":[16],"in":[17,124,185],"the":[18,44,68,73,81,90,110,114,117,163,181,186],"past":[19],"decade.":[20],"This":[21],"paper":[22],"proposes":[23],"an":[24,50],"emotion":[25],"system":[27],"based":[28],"on":[29,89,162],"analysis":[30],"of":[31,43,53,67,75,93,96,116,120],"speech":[32,38,111],"signals.":[33],"Firstly,":[34],"we":[35,48,100],"split":[36],"each":[37,66,76,97],"signal":[39],"into":[40],"overlapping":[41],"frames":[42,95],"same":[45],"length.":[46],"Next,":[47],"extract":[49],"88-dimensional":[51],"vector":[52],"audio":[54,98],"features":[55,92],"including":[56],"Mel":[57],"Frequency":[58],"Cepstral":[59],"Coefficients":[60],"(MFCC),":[61],"pitch,":[62],"intensity":[64],"for":[65],"respective":[69],"frames.":[70],"In":[71,80],"parallel,":[72],"spectrogram":[74],"frame":[77],"is":[78,122],"generated.":[79],"final":[82],"preprocessing":[83],"step,":[84],"by":[85],"applying":[86],"<i>k</i>-means":[87],"clustering":[88],"extracted":[91],"all":[94],"signal,":[99],"select":[101],"<i>k</i>":[102],"most":[103],"discriminant":[104],"frames,":[105],"namely":[106],"keyframes,":[107],"to":[108,132,180],"summarize":[109],"signal.":[112],"Then,":[113],"sequence":[115],"corresponding":[118],"spectrograms":[119],"keyframes":[121],"encapsulated":[123],"3D":[126,137,148],"tensor.":[127],"These":[128],"tensors":[129],"are":[130,160,178],"used":[131],"train":[133],"test":[135],"Convolutional":[138],"Neural":[139],"network":[140],"using":[141],"10-fold":[143],"cross-validation":[144],"approach.":[145],"The":[146,176],"proposed":[147],"CNN":[149],"two":[151],"convolutional":[152],"layers":[153],"one":[155],"fully":[156],"connected":[157],"layer.":[158],"Experiments":[159],"conducted":[161],"Surrey":[164],"Audio-Visual":[165],"Expressed":[166],"(SAVEE),":[168],"Ryerson":[169],"Multimedia":[170],"Laboratory":[171],"(RML),":[172],"eNTERFACE'05":[174],"databases.":[175],"results":[177],"superior":[179],"state-of-the-art":[182],"methods":[183],"reported":[184],"literature.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":26},{"year":2023,"cited_by_count":25},{"year":2022,"cited_by_count":33},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":21},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
