{"id":"https://openalex.org/W2021913835","doi":"https://doi.org/10.1145/2808196.2811641","title":"Multimodal Affective Dimension Prediction Using Deep Bidirectional Long Short-Term Memory Recurrent Neural Networks","display_name":"Multimodal Affective Dimension Prediction Using Deep Bidirectional Long Short-Term Memory Recurrent Neural Networks","publication_year":2015,"publication_date":"2015-10-13","ids":{"openalex":"https://openalex.org/W2021913835","doi":"https://doi.org/10.1145/2808196.2811641","mag":"2021913835"},"language":"en","primary_location":{"id":"doi:10.1145/2808196.2811641","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2808196.2811641","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Workshop on Audio/Visual Emotion Challenge","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/A5054625077","display_name":"Lang He","orcid":"https://orcid.org/0000-0003-2515-8579"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lang He","raw_affiliation_strings":["Northwestern Polytechnical University (NPU), Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University (NPU), Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102021514","display_name":"Dongmei Jiang","orcid":"https://orcid.org/0000-0002-6238-8499"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongmei Jiang","raw_affiliation_strings":["Northwestern Polytechnical University (NPU), Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University (NPU), Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100454251","display_name":"Le Yang","orcid":"https://orcid.org/0000-0001-9985-7458"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Le Yang","raw_affiliation_strings":["Northwestern Polytechnical University (NPU), Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University (NPU), Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014851312","display_name":"Ercheng Pei","orcid":"https://orcid.org/0000-0003-3582-6809"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ercheng Pei","raw_affiliation_strings":["Northwestern Polytechnical University (NPU), Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University (NPU), Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101608166","display_name":"Peng Wu","orcid":"https://orcid.org/0000-0001-7777-1485"},"institutions":[{"id":"https://openalex.org/I13469542","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04","country_code":"BE","type":"education","lineage":["https://openalex.org/I13469542"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Peng Wu","raw_affiliation_strings":["Vrije Universiteit Brussel(VUB), Brussels, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vrije Universiteit Brussel(VUB), Brussels, Belgium","institution_ids":["https://openalex.org/I13469542"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073820422","display_name":"Hichem Sahli","orcid":"https://orcid.org/0000-0002-1774-2970"},"institutions":[{"id":"https://openalex.org/I13469542","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04","country_code":"BE","type":"education","lineage":["https://openalex.org/I13469542"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Hichem Sahli","raw_affiliation_strings":["Vrije Universiteit Brussel(VUB), Brussels, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vrije Universiteit Brussel(VUB), Brussels, Belgium","institution_ids":["https://openalex.org/I13469542"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":23.6228,"has_fulltext":false,"cited_by_count":145,"citation_normalized_percentile":{"value":0.99658566,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"73","last_page":"80"},"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9945999979972839,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9713000059127808,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7125211954116821},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6832327246665955},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5522605180740356},{"id":"https://openalex.org/keywords/concordance-correlation-coefficient","display_name":"Concordance correlation coefficient","score":0.5500957369804382},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5286002159118652},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5243515968322754},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5161808133125305},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5023531913757324},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.48831674456596375},{"id":"https://openalex.org/keywords/valence","display_name":"Valence (chemistry)","score":0.43907925486564636},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39781275391578674},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14364060759544373},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11345592141151428},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.1134023666381836}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7125211954116821},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6832327246665955},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5522605180740356},{"id":"https://openalex.org/C2781059462","wikidata":"https://www.wikidata.org/wiki/Q5158906","display_name":"Concordance correlation coefficient","level":2,"score":0.5500957369804382},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5286002159118652},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5243515968322754},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5161808133125305},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5023531913757324},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.48831674456596375},{"id":"https://openalex.org/C168900304","wikidata":"https://www.wikidata.org/wiki/Q171407","display_name":"Valence (chemistry)","level":2,"score":0.43907925486564636},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39781275391578674},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14364060759544373},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11345592141151428},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.1134023666381836},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/2808196.2811641","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2808196.2811641","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Workshop on Audio/Visual Emotion Challenge","raw_type":"proceedings-article"},{"id":"pmh:oai:vubissmart:VUBISSMART:2000:250298","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306402573","display_name":"VUBIR (Vrije Universiteit Brussel)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I13469542","host_organization_name":"Vrije Universiteit Brussel","host_organization_lineage":["https://openalex.org/I13469542"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:vubissmart:VUBISSMART:2000:97785","is_oa":false,"landing_page_url":"https://biblio.vub.ac.be/vubir/multimodal-affective-dimension-prediction-using-deep-bidirectional-long-shortterm-memory-recurrent-neural-networks(03c13dba-158b-4937-bb3c-91a507381412).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306402573","display_name":"VUBIR (Vrije Universiteit Brussel)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I13469542","host_organization_name":"Vrije Universiteit Brussel","host_organization_lineage":["https://openalex.org/I13469542"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3990442515","display_name":null,"funder_award_id":"61273265","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322634","display_name":"Vrije Universiteit Brussel","ror":"https://ror.org/006e5kg04"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W211912913","https://openalex.org/W304834817","https://openalex.org/W1976066595","https://openalex.org/W1983703866","https://openalex.org/W1988088279","https://openalex.org/W1994405094","https://openalex.org/W2005708641","https://openalex.org/W2005881085","https://openalex.org/W2014915963","https://openalex.org/W2024898704","https://openalex.org/W2026243162","https://openalex.org/W2029334490","https://openalex.org/W2036309320","https://openalex.org/W2037441721","https://openalex.org/W2045528981","https://openalex.org/W2056403322","https://openalex.org/W2065015198","https://openalex.org/W2069889204","https://openalex.org/W2075708150","https://openalex.org/W2082408140","https://openalex.org/W2084253467","https://openalex.org/W2090777335","https://openalex.org/W2091828388","https://openalex.org/W2092206588","https://openalex.org/W2095540482","https://openalex.org/W2105964111","https://openalex.org/W2114388122","https://openalex.org/W2124449901","https://openalex.org/W2130162821","https://openalex.org/W2138492448","https://openalex.org/W2143612262","https://openalex.org/W2149875516","https://openalex.org/W2164186291","https://openalex.org/W2164368909","https://openalex.org/W2170027724","https://openalex.org/W2293634267","https://openalex.org/W2294797155","https://openalex.org/W2295300449","https://openalex.org/W2394662942","https://openalex.org/W3141239769","https://openalex.org/W4285719527","https://openalex.org/W6793677809"],"related_works":["https://openalex.org/W1978572805","https://openalex.org/W2383807498","https://openalex.org/W1997992934","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W1987225439","https://openalex.org/W4238188170","https://openalex.org/W3032952384","https://openalex.org/W3034302643","https://openalex.org/W1847088711"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"our":[3,191],"system":[4],"design":[5],"for":[6,82,180,215,219,230,234],"the":[7,14,21,29,35,46,70,90,97,113,116,128,132,156,160,164,181,195,205,208,223,226],"Audio-Visual":[8],"Emotion":[9],"Challenge":[10],"($AV^{+}EC$":[11],"2015).":[12],"Besides":[13],"baseline":[15,73],"features,":[16,136],"we":[17,49,137],"extract":[18,50],"from":[19,33,39,61,159],"audio":[20],"functionals":[22],"on":[23,222],"low-level":[24],"descriptors":[25],"(LLDs)":[26],"obtained":[27,209],"via":[28,163],"YAAFE":[30],"toolbox,":[31],"and":[32,55,115,125,194,217,221,232],"video":[34],"Local":[36],"Phase":[37],"Quantization":[38],"Three":[40],"Orthogonal":[41],"Planes":[42],"(LPQ-TOP)":[43],"features.":[44],"From":[45],"physiological":[47],"signals,":[48],"52":[51],"electro-cardiogram":[52],"(ECG)":[53],"features":[54,60,67,74,114,193],"22":[56],"electro-dermal":[57],"activity":[58],"(EDA)":[59],"various":[62],"analysis":[63],"domains.":[64],"The":[65],"extracted":[66],"along":[68],"with":[69,169],"$AV^{+}EC$":[71],"2015":[72],"of":[75,96,127,134,178,184],"audio,":[76],"ECG":[77],"or":[78],"EDA":[79],"are":[80,119,166],"concatenated":[81],"a":[83,139,175],"further":[84],"feature":[85,123],"selection":[86,124],"step,":[87],"in":[88,121,154],"which":[89,155],"concordance":[91],"correlation":[92,100],"coefficient":[93,101],"(CCC),":[94],"instead":[95],"usual":[98],"Pearson":[99],"(CC),":[102],"has":[103],"been":[104],"used":[105],"as":[106],"objective":[107],"function.":[108],"In":[109],"addition,":[110],"offsets":[111],"between":[112],"arousal/valence":[117],"labels":[118],"considered":[120],"both":[122],"modeling":[126],"affective":[129,185],"dimensions.":[130],"For":[131],"fusion":[133,198],"multimodal":[135,150],"propose":[138],"Deep":[140],"Bidirectional":[141],"Long":[142],"Short-Term":[143],"Memory":[144],"Recurrent":[145],"Neural":[146],"Network":[147],"(DBLSTM-RNN)":[148],"based":[149,197],"affect":[151],"prediction":[152,183],"framework,":[153],"initial":[157],"predictions":[158],"single":[161],"modalities":[162],"DBLSTM-RNNs":[165],"firstly":[167],"smoothed":[168],"Gaussian":[170],"smoothing,":[171],"then":[172],"input":[173],"into":[174],"second":[176],"layer":[177],"DBLSTM-RNN":[179,196],"final":[182],"state.":[186],"Experimental":[187],"results":[188],"show":[189],"that":[190],"proposed":[192],"framework":[199],"obtain":[200],"very":[201],"promising":[202],"results.":[203],"On":[204],"development":[206],"set,":[207,225],"CCC":[210,227],"is":[211,228],"up":[212],"to":[213],"0.824":[214],"arousal":[216,231],"0.688":[218],"valence,":[220],"test":[224],"0.747":[229],"0.609":[233],"valence.":[235]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":20},{"year":2018,"cited_by_count":27},{"year":2017,"cited_by_count":24},{"year":2016,"cited_by_count":22}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}