{"id":"https://openalex.org/W4221072225","doi":"https://doi.org/10.1109/icce53296.2022.9730534","title":"Emotion Recognition from Raw Speech Signals Using 2D CNN with Deep Metric Learning","display_name":"Emotion Recognition from Raw Speech Signals Using 2D CNN with Deep Metric Learning","publication_year":2022,"publication_date":"2022-01-07","ids":{"openalex":"https://openalex.org/W4221072225","doi":"https://doi.org/10.1109/icce53296.2022.9730534"},"language":"en","primary_location":{"id":"doi:10.1109/icce53296.2022.9730534","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce53296.2022.9730534","pdf_url":null,"source":{"id":"https://openalex.org/S4363608007","display_name":"2022 IEEE International Conference on Consumer Electronics (ICCE)","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 Consumer Electronics (ICCE)","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/A5073509926","display_name":"Bogdan Mocanu","orcid":"https://orcid.org/0000-0002-2751-0954"},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":true,"raw_author_name":"Bogdan Mocanu","raw_affiliation_strings":["University “Politehnica” of Bucharest,Faculty of ETTL,Department of Telecommunications,Romania","of Bucharest, Romania","Politehnica&#x201D"],"affiliations":[{"raw_affiliation_string":"University “Politehnica” of Bucharest,Faculty of ETTL,Department of Telecommunications,Romania","institution_ids":["https://openalex.org/I61641377"]},{"raw_affiliation_string":"of Bucharest, Romania","institution_ids":[]},{"raw_affiliation_string":"Politehnica&#x201D","institution_ids":["https://openalex.org/I61641377"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025204322","display_name":"Ruxandra \u021aapu","orcid":"https://orcid.org/0000-0003-3170-4150"},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Ruxandra Tapu","raw_affiliation_strings":["Institut Polytechnique de Paris, Télécom SudParis,ARTEMIS Department,Évry,France,91000","Politehnica&#x201D","l&#x00E9","vry, France","of Bucharest, Romania","Department of Telecommunications, Faculty of ETTL, University &#x201C","com SudParis, &#x00C9"],"affiliations":[{"raw_affiliation_string":"Institut Polytechnique de Paris, Télécom SudParis,ARTEMIS Department,Évry,France,91000","institution_ids":[]},{"raw_affiliation_string":"Politehnica&#x201D","institution_ids":["https://openalex.org/I61641377"]},{"raw_affiliation_string":"l&#x00E9","institution_ids":[]},{"raw_affiliation_string":"vry, France","institution_ids":[]},{"raw_affiliation_string":"of Bucharest, Romania","institution_ids":[]},{"raw_affiliation_string":"Department of Telecommunications, Faculty of ETTL, University &#x201C","institution_ids":[]},{"raw_affiliation_string":"com SudParis, &#x00C9","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5073509926"],"corresponding_institution_ids":["https://openalex.org/I61641377"],"apc_list":null,"apc_paid":null,"fwci":0.8315,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.7243083,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9998999834060669,"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":0.9998999834060669,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.996399998664856,"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":0.9918000102043152,"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/computer-science","display_name":"Computer science","score":0.7775825262069702},{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.7677709460258484},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.7194223403930664},{"id":"https://openalex.org/keywords/utterance","display_name":"Utterance","score":0.6606647372245789},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.65984046459198},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6486365795135498},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.5565502047538757},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5301951766014099},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.48715969920158386},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4414183497428894},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.43744468688964844},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4275195002555847},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.34930381178855896}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7775825262069702},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.7677709460258484},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.7194223403930664},{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.6606647372245789},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.65984046459198},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6486365795135498},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.5565502047538757},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5301951766014099},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.48715969920158386},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4414183497428894},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.43744468688964844},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4275195002555847},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.34930381178855896},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icce53296.2022.9730534","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce53296.2022.9730534","pdf_url":null,"source":{"id":"https://openalex.org/S4363608007","display_name":"2022 IEEE International Conference on Consumer Electronics (ICCE)","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 Consumer Electronics (ICCE)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-03937087v1","is_oa":false,"landing_page_url":"https://hal.science/hal-03937087","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2022 IEEE International Conference on Consumer Electronics (ICCE), Jan 2022, Las Vegas, United States. pp.1-5, ⟨10.1109/ICCE53296.2022.9730534⟩","raw_type":"Conference papers"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1923034539","https://openalex.org/W2003238582","https://openalex.org/W2030931454","https://openalex.org/W2074788634","https://openalex.org/W2087618018","https://openalex.org/W2155986100","https://openalex.org/W2194775991","https://openalex.org/W2324037684","https://openalex.org/W2395587375","https://openalex.org/W2625297138","https://openalex.org/W2746241180","https://openalex.org/W2746978487","https://openalex.org/W2786779322","https://openalex.org/W2803193013","https://openalex.org/W2889065492","https://openalex.org/W2896584247","https://openalex.org/W2916104401","https://openalex.org/W2969889150","https://openalex.org/W2981087920","https://openalex.org/W3094312606","https://openalex.org/W3097255602","https://openalex.org/W3106506544","https://openalex.org/W3161565210","https://openalex.org/W6687483927"],"related_works":["https://openalex.org/W2530685530","https://openalex.org/W2011227383","https://openalex.org/W4375868962","https://openalex.org/W2088854863","https://openalex.org/W1976719989","https://openalex.org/W2942893872","https://openalex.org/W2065606036","https://openalex.org/W2184242386","https://openalex.org/W2325729322","https://openalex.org/W1923358586"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3],"have":[4],"introduced":[5],"a":[6,32,39,54],"novel":[7],"emotion":[8],"recognition":[9],"framework":[10],"from":[11],"raw":[12],"speech":[13],"signals.":[14],"The":[15,26,51,67],"system":[16,52],"is":[17,28],"based":[18],"on":[19,71],"ResNet":[20],"architecture":[21],"fed":[22],"with":[23,31,83],"spectrogram":[24],"inputs.":[25],"CNN":[27],"further":[29],"extended":[30],"GhostVLAD":[33],"feature":[34],"aggregation":[35],"layer":[36],"that":[37,58],"extracts":[38],"single,":[40],"fixed":[41],"size":[42],"descriptor":[43],"constructed":[44],"at":[45],"the":[46,49,60,80],"level":[47],"of":[48,65,87],"utterance.":[50],"adopts":[53],"sentiment":[55],"metric":[56],"loss":[57],"integrates":[59],"relations":[61],"between":[62],"various":[63],"classes":[64],"emotions.":[66],"experimental":[68],"evaluation":[69],"conducted":[70],"two":[72],"publicly":[73],"available":[74],"databases:":[75],"RAVDESS":[76],"and":[77,89],"CREMA-D":[78],"validates":[79],"proposed":[81],"methodology":[82],"average":[84],"accuracy":[85],"scores":[86],"82%":[88],"63%,":[90],"respectively.":[91]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}