{"id":"https://openalex.org/W4372340881","doi":"https://doi.org/10.1109/icassp49357.2023.10097238","title":"Semi-Supervised Learning with Per-Class Adaptive Confidence Scores for Acoustic Environment Classification with Imbalanced Data","display_name":"Semi-Supervised Learning with Per-Class Adaptive Confidence Scores for Acoustic Environment Classification with Imbalanced Data","publication_year":2023,"publication_date":"2023-05-05","ids":{"openalex":"https://openalex.org/W4372340881","doi":"https://doi.org/10.1109/icassp49357.2023.10097238"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49357.2023.10097238","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icassp49357.2023.10097238","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research.tue.nl/files/308514139/Semi-Supervised_Learning_with_Per-Class_Adaptive_Confidence_Scores_for_Acoustic_Environment_Classification_with_Imbalanced_Data_1_.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058694041","display_name":"Luan Vin\u00edcius Fiorio","orcid":"https://orcid.org/0000-0003-4964-6120"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Luan Vin\u00edcius Fiorio","raw_affiliation_strings":["Eindhoven University of Technology,Eindhoven,The Netherlands,5600 MB"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology,Eindhoven,The Netherlands,5600 MB","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074300146","display_name":"Boris Karanov","orcid":"https://orcid.org/0000-0003-1657-8109"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Boris Karanov","raw_affiliation_strings":["Eindhoven University of Technology,Eindhoven,The Netherlands,5600 MB"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology,Eindhoven,The Netherlands,5600 MB","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041292629","display_name":"Johan David","orcid":null},"institutions":[{"id":"https://openalex.org/I109147379","display_name":"NXP (Netherlands)","ror":"https://ror.org/059be4e97","country_code":"NL","type":"company","lineage":["https://openalex.org/I109147379"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Johan David","raw_affiliation_strings":["NXP Semiconductors,Eindhoven,The Netherlands,5656 AE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NXP Semiconductors,Eindhoven,The Netherlands,5656 AE","institution_ids":["https://openalex.org/I109147379"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038293387","display_name":"Wim van Houtum","orcid":"https://orcid.org/0000-0001-5288-6147"},"institutions":[{"id":"https://openalex.org/I109147379","display_name":"NXP (Netherlands)","ror":"https://ror.org/059be4e97","country_code":"NL","type":"company","lineage":["https://openalex.org/I109147379"]},{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Wim van Houtum","raw_affiliation_strings":["Eindhoven University of Technology,Eindhoven,The Netherlands,5600 MB","NXP Semiconductors, Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology,Eindhoven,The Netherlands,5600 MB","institution_ids":["https://openalex.org/I83019370"]},{"raw_affiliation_string":"NXP Semiconductors, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I109147379"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087001469","display_name":"F. Widdershoven","orcid":null},"institutions":[{"id":"https://openalex.org/I109147379","display_name":"NXP (Netherlands)","ror":"https://ror.org/059be4e97","country_code":"NL","type":"company","lineage":["https://openalex.org/I109147379"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Frans Widdershoven","raw_affiliation_strings":["NXP Semiconductors,Eindhoven,The Netherlands,5656 AE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NXP Semiconductors,Eindhoven,The Netherlands,5656 AE","institution_ids":["https://openalex.org/I109147379"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040056296","display_name":"Ronald M. Aarts","orcid":"https://orcid.org/0000-0003-3194-0700"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ronald M. Aarts","raw_affiliation_strings":["Eindhoven University of Technology,Eindhoven,The Netherlands,5600 MB"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology,Eindhoven,The Netherlands,5600 MB","institution_ids":["https://openalex.org/I83019370"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"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/T11309","display_name":"Music and Audio Processing","score":1.0,"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":1.0,"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":0.9994999766349792,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9803000092506409,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7030097246170044},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.657521665096283},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6514382362365723},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5510016679763794},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5367222428321838},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.501274824142456},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5009706020355225},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4849129319190979},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4611930847167969},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.4357073903083801},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4331872761249542},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.4321228265762329}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7030097246170044},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.657521665096283},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6514382362365723},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5510016679763794},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5367222428321838},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.501274824142456},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5009706020355225},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4849129319190979},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4611930847167969},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.4357073903083801},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4331872761249542},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.4321228265762329},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icassp49357.2023.10097238","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icassp49357.2023.10097238","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.tue.nl:openaire_cris_publications/bc8ac5bb-a449-4e21-ae76-cad81548f1b3","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/bc8ac5bb-a449-4e21-ae76-cad81548f1b3","pdf_url":"https://research.tue.nl/files/308514139/Semi-Supervised_Learning_with_Per-Class_Adaptive_Confidence_Scores_for_Acoustic_Environment_Classification_with_Imbalanced_Data_1_.pdf","source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","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":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:pure.tue.nl:openaire_cris_publications/bc8ac5bb-a449-4e21-ae76-cad81548f1b3","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/bc8ac5bb-a449-4e21-ae76-cad81548f1b3","pdf_url":"https://research.tue.nl/files/308514139/Semi-Supervised_Learning_with_Per-Class_Adaptive_Confidence_Scores_for_Acoustic_Environment_Classification_with_Imbalanced_Data_1_.pdf","source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","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":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4372340881.pdf"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W2006129368","https://openalex.org/W2031647436","https://openalex.org/W2038484192","https://openalex.org/W2509712787","https://openalex.org/W2676925568","https://openalex.org/W2798869704","https://openalex.org/W2804829424","https://openalex.org/W2931364255","https://openalex.org/W2953070460","https://openalex.org/W2963177459","https://openalex.org/W2984353870","https://openalex.org/W3034679848","https://openalex.org/W3090558194","https://openalex.org/W3136922302","https://openalex.org/W3189134925","https://openalex.org/W3203613879","https://openalex.org/W4224918818","https://openalex.org/W6733814495","https://openalex.org/W6751711788","https://openalex.org/W6779403271"],"related_works":["https://openalex.org/W1586607209","https://openalex.org/W122912556","https://openalex.org/W4312414840","https://openalex.org/W2621411691","https://openalex.org/W2271357838","https://openalex.org/W2556866732","https://openalex.org/W2328989934","https://openalex.org/W2348322200","https://openalex.org/W2981952041","https://openalex.org/W3148060700"],"abstract_inverted_index":{"In":[0],"this":[1,56],"paper,":[2],"we":[3,58],"concentrate":[4],"on":[5,132],"the":[6,14,71,80,116,123,127],"per-class":[7,120],"accuracy":[8,73,121],"of":[9,16,30,70,119,126],"neural":[10],"network-based":[11],"classification":[12],"in":[13,40],"context":[15],"identifying":[17],"acoustic":[18],"environments.":[19],"Even":[20],"a":[21,67],"fully":[22,104],"supervised":[23,105],"learning":[24,49,106],"framework":[25],"with":[26],"an":[27,60],"equal":[28],"amount":[29],"data":[31],"for":[32,63,94,112],"each":[33],"class":[34,41],"can":[35,91],"lead":[36],"to":[37,103],"significant":[38],"differences":[39],"accuracies.":[42],"This":[43],"is":[44,88],"then":[45],"amplified":[46],"by":[47],"semi-supervised":[48],"using":[50],"naturally":[51],"imbalanced":[52],"data.":[53],"To":[54],"address":[55],"problem,":[57],"propose":[59],"adaptive":[61],"method":[62,87],"pseudo-label":[64],"selection":[65],"via":[66],"straightforward":[68],"optimization":[69],"validation":[72],"per":[74],"class,":[75],"aimed":[76],"specifically":[77],"at":[78],"reducing":[79],"variance":[81],"between":[82],"different":[83],"classes.":[84],"The":[85],"proposed":[86],"general":[89],"and":[90,98,122],"be":[92],"applied":[93],"both":[95],"maximum":[96],"probability":[97],"entropy-based":[99],"confidence":[100],"criteria.":[101],"Compared":[102],"as":[107,109],"well":[108],"state-of-the-art":[110],"methods":[111],"pseudo-labeling,":[113],"it":[114],"achieves":[115],"lowest":[117],"variances":[118],"highest":[124],"accuracies":[125],"minority":[128],"classes":[129],"when":[130],"tested":[131],"common":[133],"publicly":[134],"available":[135],"environment":[136],"sound":[137],"databases.":[138]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}
