{"id":"https://openalex.org/W4408345763","doi":"https://doi.org/10.1109/icassp49660.2025.10890038","title":"Exploiting Wavelet Scattering Transform & Squeeze-Excitation Blocks with Cross-Modal Attention for Multi-modal Emotion Recognition","display_name":"Exploiting Wavelet Scattering Transform & Squeeze-Excitation Blocks with Cross-Modal Attention for Multi-modal Emotion Recognition","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408345763","doi":"https://doi.org/10.1109/icassp49660.2025.10890038"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10890038","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10890038","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101900841","display_name":"Junchen Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Junchen Liu","raw_affiliation_strings":["The University of Auckland,Department of Electrical, Computer and Software Engineering,New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Auckland,Department of Electrical, Computer and Software Engineering,New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082577862","display_name":"Jesin James","orcid":"https://orcid.org/0000-0003-0024-9597"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Jesin James","raw_affiliation_strings":["The University of Auckland,Department of Electrical, Computer and Software Engineering,New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Auckland,Department of Electrical, Computer and Software Engineering,New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107339473","display_name":"Karan Nathwani","orcid":"https://orcid.org/0000-0002-0678-2328"},"institutions":[{"id":"https://openalex.org/I4210127441","display_name":"Indian Institute of Technology Jammu","ror":"https://ror.org/02f0vsw63","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210127441"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Karan Nathwani","raw_affiliation_strings":["India Institute of Technology Jammu,Department of Electrical Engineering,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"India Institute of Technology Jammu,Department of Electrical Engineering,India","institution_ids":["https://openalex.org/I4210127441"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01816444,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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":0.7894999980926514,"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":0.7894999980926514,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.7009999752044678,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.8699297904968262},{"id":"https://openalex.org/keywords/excitation","display_name":"Excitation","score":0.6044463515281677},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5520232915878296},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5389524102210999},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5106022953987122},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.3983477056026459},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32576411962509155},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3205755352973938},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.21528178453445435},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.191867858171463},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16733330488204956},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.09441447257995605}],"concepts":[{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.8699297904968262},{"id":"https://openalex.org/C83581075","wikidata":"https://www.wikidata.org/wiki/Q1361503","display_name":"Excitation","level":2,"score":0.6044463515281677},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5520232915878296},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5389524102210999},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5106022953987122},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.3983477056026459},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32576411962509155},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3205755352973938},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.21528178453445435},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.191867858171463},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16733330488204956},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.09441447257995605},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10890038","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10890038","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320801","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2093231248","https://openalex.org/W2146334809","https://openalex.org/W2191779130","https://openalex.org/W2336829316","https://openalex.org/W2584561145","https://openalex.org/W2785960144","https://openalex.org/W2937675449","https://openalex.org/W2967525496","https://openalex.org/W3035570025","https://openalex.org/W3081192838","https://openalex.org/W3081213963","https://openalex.org/W3092531824","https://openalex.org/W3126576784","https://openalex.org/W3172353912","https://openalex.org/W3194672963","https://openalex.org/W3209432345","https://openalex.org/W4213040646","https://openalex.org/W4223944998","https://openalex.org/W4225878721","https://openalex.org/W4288050061","https://openalex.org/W4362721809","https://openalex.org/W4384027407","https://openalex.org/W4389733467","https://openalex.org/W4391021402","https://openalex.org/W4392903618","https://openalex.org/W4400228323","https://openalex.org/W4402112154","https://openalex.org/W4404178029","https://openalex.org/W6757542112"],"related_works":["https://openalex.org/W2382174632","https://openalex.org/W2129959498","https://openalex.org/W2784060934","https://openalex.org/W2902714807","https://openalex.org/W2537489131","https://openalex.org/W2046633342","https://openalex.org/W2394084632","https://openalex.org/W2358293514","https://openalex.org/W2059273319","https://openalex.org/W2077021924"],"abstract_inverted_index":{"Multi-modal":[0],"emotion":[1,164],"recognition":[2,165],"(MER)":[3],"is":[4],"crucial":[5],"for":[6,17],"improving":[7],"human-computer":[8],"interaction.":[9],"Convolutional":[10],"neural":[11],"networks":[12],"(CNNs)":[13],"are":[14],"the":[15,59,63,134,145,152,158],"mainstream":[16],"MER":[18,39],"tasks,":[19],"but":[20],"they":[21],"require":[22],"large":[23],"databases,":[24],"extensive":[25],"memory,":[26],"and":[27,81,92,98,104,110,127,140,160,169,175],"significant":[28],"energy,":[29],"limiting":[30],"their":[31],"practical":[32],"use.":[33],"This":[34],"paper":[35],"proposes":[36],"a":[37,73,78,111],"novel":[38],"system":[40,60,87,120],"that":[41,118,133],"leverages":[42],"wavelet":[43],"scattering":[44],"transform":[45],"(WST)":[46],"to":[47,178],"address":[48],"these":[49],"challenges,":[50],"achieving":[51],"improved":[52,173],"performance":[53,174],"with":[54,106,148],"lower":[55],"computational":[56],"consumption.":[57],"Moreover,":[58],"benefits":[61],"from":[62,95],"noise":[64,125],"robustness":[65,176],"provided":[66],"by":[67],"WST.":[68],"By":[69],"integrating":[70],"WST":[71],"as":[72],"non-trainable":[74],"initial":[75],"layer":[76],"in":[77],"CNN":[79],"model":[80],"employing":[82],"an":[83],"encoder":[84],"module,":[85],"our":[86,119],"effectively":[88],"captures":[89],"time-frequency,":[90],"local":[91],"high-level":[93],"features":[94,143],"both":[96],"speech":[97],"video.":[99],"We":[100],"enhance":[101],"feature":[102],"integration":[103],"representation":[105],"cross-modal":[107],"attention":[108],"(CMA)":[109],"squeeze-and-excitation":[112],"(SE)":[113],"block.":[114],"The":[115],"results":[116,156],"demonstrate":[117],"performs":[121],"consistently":[122],"across":[123],"varying":[124],"levels":[126],"duration":[128],"thresholds.":[129],"Ablation":[130],"studies":[131],"reveal":[132],"combination":[135],"of":[136,167],"MFCC,":[137],"Mel":[138,149],"spectrogram,":[139],"raw":[141],"waveform":[142],"yields":[144],"highest":[146],"accuracy,":[147],"spectrogram":[150],"being":[151],"most":[153],"influential.":[154],"Experimental":[155],"on":[157],"IEMOCAP":[159],"RAVDESS":[161],"databases":[162],"achieve":[163],"accuracy":[166],"83.2%":[168],"97.8%,":[170],"respectively,":[171],"showcasing":[172],"compared":[177],"state-of-the-art":[179],"models,":[180],"while":[181],"using":[182],"fewer":[183],"trainable":[184],"parameters.":[185]},"counts_by_year":[],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}