{"id":"https://openalex.org/W4405928096","doi":"https://doi.org/10.3390/sym17010049","title":"Local Time-Frequency Feature Fusion Using Cross-Attention for Acoustic Scene Classification","display_name":"Local Time-Frequency Feature Fusion Using Cross-Attention for Acoustic Scene Classification","publication_year":2024,"publication_date":"2024-12-30","ids":{"openalex":"https://openalex.org/W4405928096","doi":"https://doi.org/10.3390/sym17010049"},"language":"en","primary_location":{"id":"doi:10.3390/sym17010049","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17010049","pdf_url":"https://www.mdpi.com/2073-8994/17/1/49/pdf?version=1735566218","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"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":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/17/1/49/pdf?version=1735566218","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047505143","display_name":"Rong Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Rong Huang","raw_affiliation_strings":["Information Construction and Management Office, Nanjing University of Posts and Telecommunications, Nanjing 210049, China"],"affiliations":[{"raw_affiliation_string":"Information Construction and Management Office, Nanjing University of Posts and Telecommunications, Nanjing 210049, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052217750","display_name":"Yue Xie","orcid":"https://orcid.org/0000-0002-5750-5873"},"institutions":[{"id":"https://openalex.org/I2799736854","display_name":"Nanjing Institute of Technology","ror":"https://ror.org/00n6txq60","country_code":"CN","type":"education","lineage":["https://openalex.org/I2799736854"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Xie","raw_affiliation_strings":["School of Communication and Artificial Intelligence, School of Integrated Circuits, Nanjing Institute of Technology, Nanjing 211167, China"],"affiliations":[{"raw_affiliation_string":"School of Communication and Artificial Intelligence, School of Integrated Circuits, Nanjing Institute of Technology, Nanjing 211167, China","institution_ids":["https://openalex.org/I2799736854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103130740","display_name":"Pengxu Jiang","orcid":"https://orcid.org/0000-0002-2946-7806"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengxu Jiang","raw_affiliation_strings":["School of Information Science and Engineering, Southeast University, Nanjing 210018, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Southeast University, Nanjing 210018, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5047505143"],"corresponding_institution_ids":["https://openalex.org/I41198531"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.3628,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.61101422,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"17","issue":"1","first_page":"49","last_page":"49"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9998000264167786,"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/T10860","display_name":"Speech and Audio Processing","score":0.9962000250816345,"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/T13996","display_name":"Diverse Musicological Studies","score":0.9839000105857849,"subfield":{"id":"https://openalex.org/subfields/1210","display_name":"Music"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7980645895004272},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7102038860321045},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6028230786323547},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5927515625953674},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5703054070472717},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5131955742835999},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.48993009328842163},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4728658199310303},{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.4630899727344513},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3289002478122711}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7980645895004272},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7102038860321045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6028230786323547},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5927515625953674},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5703054070472717},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5131955742835999},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.48993009328842163},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4728658199310303},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.4630899727344513},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3289002478122711},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/sym17010049","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17010049","pdf_url":"https://www.mdpi.com/2073-8994/17/1/49/pdf?version=1735566218","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"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":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:212babf0aa044a13875cb97b95fee702","is_oa":true,"landing_page_url":"https://doaj.org/article/212babf0aa044a13875cb97b95fee702","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 17, Iss 1, p 49 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/sym17010049","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17010049","pdf_url":"https://www.mdpi.com/2073-8994/17/1/49/pdf?version=1735566218","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"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":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4405928096.pdf","grobid_xml":"https://content.openalex.org/works/W4405928096.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W78111734","https://openalex.org/W1997527178","https://openalex.org/W2086384421","https://openalex.org/W2157331557","https://openalex.org/W2194775991","https://openalex.org/W2517626051","https://openalex.org/W2890718983","https://openalex.org/W2922137896","https://openalex.org/W2953141561","https://openalex.org/W3027732850","https://openalex.org/W3162320900","https://openalex.org/W3201373735","https://openalex.org/W4238637301","https://openalex.org/W4285178505","https://openalex.org/W4392903770","https://openalex.org/W4402592916","https://openalex.org/W4405304594","https://openalex.org/W6603154609"],"related_works":["https://openalex.org/W2530685530","https://openalex.org/W4375868962","https://openalex.org/W2011227383","https://openalex.org/W2088854863","https://openalex.org/W4402568167","https://openalex.org/W3179495260","https://openalex.org/W1976719989","https://openalex.org/W3127543252","https://openalex.org/W2065606036","https://openalex.org/W2788292821"],"abstract_inverted_index":{"To":[0],"address":[1],"the":[2,37,46,54,61,71,102,113,122,130,136],"interdependence":[3],"of":[4,64,73,116,132],"local":[5,51],"time-frequency":[6,14,103],"information":[7],"in":[8,88],"audio":[9,24,89],"scene":[10,25,90],"recognition,":[11],"a":[12,35,96],"segment-based":[13],"feature":[15],"fusion":[16],"method":[17],"based":[18],"on":[19,60,121],"cross-attention":[20],"is":[21,27],"proposed.":[22],"Since":[23],"recognition":[26,91],"highly":[28],"sensitive":[29],"to":[30,49,58,69,99],"individual":[31],"sound":[32,66],"events":[33],"within":[34],"scene,":[36],"input":[38],"features":[39,104,117],"are":[40,85],"segmented":[41],"into":[42],"multiple":[43],"segments":[44],"along":[45],"time":[47,62],"dimension":[48],"obtain":[50,101],"features,":[52],"allowing":[53],"subsequent":[55],"attention":[56],"mechanism":[57],"focus":[59],"slices":[63],"key":[65],"events.":[67],"Furthermore,":[68],"leverage":[70],"advantages":[72],"both":[74],"convolutional":[75],"neural":[76,81],"networks":[77,82],"(CNNs)":[78],"and":[79,108,110,125,143],"recurrent":[80],"(RNNs),":[83],"which":[84],"mainstream":[86],"structures":[87],"tasks,":[92],"this":[93,133],"paper":[94],"employs":[95],"symmetry":[97],"structure":[98],"separately":[100],"output":[105],"by":[106,140],"CNNs":[107],"RNNs":[109],"then":[111],"fuses":[112],"two":[114],"sets":[115],"using":[118],"cross-attention.":[119],"Experiments":[120],"TUT2018,":[123],"TAU2019,":[124],"TAU2020":[126],"datasets":[127],"demonstrate":[128],"that":[129],"performance":[131],"algorithm":[134],"improves":[135],"official":[137],"baseline":[138],"results":[139],"17.78%,":[141],"15.95%,":[142],"20.13%,":[144],"respectively.":[145]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-11T06:11:40.159057","created_date":"2025-10-10T00:00:00"}
