{"id":"https://openalex.org/W3198690230","doi":"https://doi.org/10.21437/interspeech.2021-1052","title":"Cough-Based COVID-19 Detection with Contextual Attention Convolutional Neural Networks and Gender Information","display_name":"Cough-Based COVID-19 Detection with Contextual Attention Convolutional Neural Networks and Gender Information","publication_year":2021,"publication_date":"2021-08-27","ids":{"openalex":"https://openalex.org/W3198690230","doi":"https://doi.org/10.21437/interspeech.2021-1052","mag":"3198690230"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2021-1052","is_oa":true,"landing_page_url":"https://doi.org/10.21437/interspeech.2021-1052","pdf_url":"https://www.isca-archive.org/interspeech_2021/mallolragolta21_interspeech.html","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2021","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.isca-archive.org/interspeech_2021/mallolragolta21_interspeech.html","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048720202","display_name":"Adria Mallol-Ragolta","orcid":"https://orcid.org/0000-0001-6855-485X"},"institutions":[{"id":"https://openalex.org/I179225836","display_name":"University of Augsburg","ror":"https://ror.org/03p14d497","country_code":"DE","type":"education","lineage":["https://openalex.org/I179225836"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Adria Mallol-Ragolta","raw_affiliation_strings":["EIHW -Chair of Embedded Intelligence for Health Care & Wellbeing, University of Augsburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EIHW -Chair of Embedded Intelligence for Health Care & Wellbeing, University of Augsburg, Germany","institution_ids":["https://openalex.org/I179225836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101406726","display_name":"H. Esteban Cuesta","orcid":"https://orcid.org/0000-0002-1449-457X"},"institutions":[{"id":"https://openalex.org/I170486558","display_name":"Universitat Pompeu Fabra","ror":"https://ror.org/04n0g0b29","country_code":"ES","type":"education","lineage":["https://openalex.org/I170486558"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Helena Cuesta","raw_affiliation_strings":["MTG -Music Technology Group, Universitat Pompeu Fabra, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MTG -Music Technology Group, Universitat Pompeu Fabra, Spain","institution_ids":["https://openalex.org/I170486558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103887030","display_name":"Emilia G\u00f3mez","orcid":null},"institutions":[{"id":"https://openalex.org/I170486558","display_name":"Universitat Pompeu Fabra","ror":"https://ror.org/04n0g0b29","country_code":"ES","type":"education","lineage":["https://openalex.org/I170486558"]},{"id":"https://openalex.org/I4210166174","display_name":"Joint Research Center","ror":"https://ror.org/05a4nj078","country_code":"ES","type":"government","lineage":["https://openalex.org/I1320481043","https://openalex.org/I2800387288","https://openalex.org/I4210161702","https://openalex.org/I4210166174"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Emilia G\u00f3mez","raw_affiliation_strings":["MTG -Music Technology Group, Universitat Pompeu Fabra, Spain","Joint Research Centre, European Commission, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MTG -Music Technology Group, Universitat Pompeu Fabra, Spain","institution_ids":["https://openalex.org/I170486558"]},{"raw_affiliation_string":"Joint Research Centre, European Commission, Spain","institution_ids":["https://openalex.org/I4210166174"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043060302","display_name":"Bj\u00f6rn W. Schuller","orcid":"https://orcid.org/0000-0002-6478-8699"},"institutions":[{"id":"https://openalex.org/I179225836","display_name":"University of Augsburg","ror":"https://ror.org/03p14d497","country_code":"DE","type":"education","lineage":["https://openalex.org/I179225836"]},{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["DE","GB"],"is_corresponding":false,"raw_author_name":"Bj\u00f6rn W. Schuller","raw_affiliation_strings":["EIHW -Chair of Embedded Intelligence for Health Care & Wellbeing, University of Augsburg, Germany","GLAM -Group on Language, Audio & Music, Imperial College London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EIHW -Chair of Embedded Intelligence for Health Care & Wellbeing, University of Augsburg, Germany","institution_ids":["https://openalex.org/I179225836"]},{"raw_affiliation_string":"GLAM -Group on Language, Audio & Music, Imperial College London, UK","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"941","last_page":"945"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10654","display_name":"Pneumonia and Respiratory Infections","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11243","display_name":"Respiratory viral infections research","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/spectrogram","display_name":"Spectrogram","score":0.8691622018814087},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8152676820755005},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7111753225326538},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6519389152526855},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.5836669206619263},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5667827725410461},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.5499288439750671},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5166154503822327},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5147950053215027},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.49167948961257935},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4855220317840576},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4122177064418793},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.4110344648361206},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40595459938049316},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3338085412979126},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.07749363780021667},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.060854166746139526}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.8691622018814087},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8152676820755005},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7111753225326538},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6519389152526855},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.5836669206619263},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5667827725410461},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.5499288439750671},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5166154503822327},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5147950053215027},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.49167948961257935},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4855220317840576},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4122177064418793},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.4110344648361206},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40595459938049316},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3338085412979126},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.07749363780021667},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.060854166746139526},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.21437/interspeech.2021-1052","is_oa":true,"landing_page_url":"https://doi.org/10.21437/interspeech.2021-1052","pdf_url":"https://www.isca-archive.org/interspeech_2021/mallolragolta21_interspeech.html","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2021","raw_type":"proceedings-article"},{"id":"pmh:oai:repositori-api.upf.edu:10230/55988","is_oa":false,"landing_page_url":"http://hdl.handle.net/10230/55988","pdf_url":null,"source":{"id":"https://openalex.org/S4306402615","display_name":"Repositori digital de la UPF (Universitat Pompeu Fabra)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I170486558","host_organization_name":"Universitat Pompeu Fabra","host_organization_lineage":["https://openalex.org/I170486558"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:uni-augsburg.opus-bayern.de:91652","is_oa":false,"landing_page_url":"https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/91652","pdf_url":null,"source":{"id":"https://openalex.org/S4306400930","display_name":"OPUS (Augsburg University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I119916105","host_organization_name":"Augsburg University","host_organization_lineage":["https://openalex.org/I119916105"],"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":"bookpart"},{"id":"pmh:oai:zenodo.org:5801380","is_oa":true,"landing_page_url":"https://doi.org/10.21437/Interspeech.2021-1052","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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":"INTERSPEECH 2021, International Speech Communication Association 2021, Brno Czechia, 30 August 2021- 3 September 2021","raw_type":"info:eu-repo/semantics/conferencePaper"}],"best_oa_location":{"id":"doi:10.21437/interspeech.2021-1052","is_oa":true,"landing_page_url":"https://doi.org/10.21437/interspeech.2021-1052","pdf_url":"https://www.isca-archive.org/interspeech_2021/mallolragolta21_interspeech.html","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2021","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6499999761581421,"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5"}],"awards":[{"id":"https://openalex.org/G2136136281","display_name":"Smart environments for person-centered sustainable work and well-being","funder_award_id":"826506","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G2740681022","display_name":null,"funder_award_id":"2018FI","funder_id":"https://openalex.org/F4320321505","funder_display_name":"Generalitat de Catalunya"},{"id":"https://openalex.org/G4593709364","display_name":"Towards Richer Online Music Public-domain Archives","funder_award_id":"770376","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320321505","display_name":"Generalitat de Catalunya","ror":"https://ror.org/01bg62x04"},{"id":"https://openalex.org/F4320334830","display_name":"Ag\u00e8ncia de Gesti\u00f3 d'Ajuts Universitaris i de Recerca","ror":"https://ror.org/01n4pqe45"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3198690230.pdf","grobid_xml":"https://content.openalex.org/works/W3198690230.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W2023582935","https://openalex.org/W2194775991","https://openalex.org/W2470673105","https://openalex.org/W2755206682","https://openalex.org/W2768137799","https://openalex.org/W2786800255","https://openalex.org/W2856863501","https://openalex.org/W2889056793","https://openalex.org/W2895271672","https://openalex.org/W2972968667","https://openalex.org/W2981677410","https://openalex.org/W3016996100","https://openalex.org/W3021675042","https://openalex.org/W3028563376","https://openalex.org/W3030621456","https://openalex.org/W3035378948","https://openalex.org/W3036552116","https://openalex.org/W3036688711","https://openalex.org/W3080906603","https://openalex.org/W3083972167","https://openalex.org/W3089168916","https://openalex.org/W3096215591","https://openalex.org/W3100327638","https://openalex.org/W3104004606","https://openalex.org/W3105837102","https://openalex.org/W3134945014","https://openalex.org/W3139254073","https://openalex.org/W3198173682","https://openalex.org/W4206458399","https://openalex.org/W6687483927","https://openalex.org/W6912594804"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W3099765033","https://openalex.org/W2997155179"],"abstract_inverted_index":{"The":[0,144],"aim":[1,52],"of":[2,47,61,71,77,83,90,107,139,169],"this":[3,65],"contribution":[4],"is":[5,151],"to":[6,31,53,115],"automatically":[7],"detect":[8],"COVID-19":[9,19,56],"patients":[10],"by":[11,123],"analysing":[12,43],"the":[13,21,29,36,44,51,58,69,74,84,88,105,111,117,124,131,135,140,148,154,166],"acoustic":[14],"information":[15,34],"embedded":[16],"in":[17,73],"coughs.":[18],"affects":[20],"respiratory":[22],"system,":[23],"and,":[24],"consequently,":[25],"respiratory-related":[26],"signals":[27],"have":[28],"potential":[30],"contain":[32],"salient":[33],"for":[35,134],"task":[37],"at":[38,172],"hand.":[39],"We":[40,126],"focus":[41],"on":[42,130,147],"spectrogram":[45],"representations":[46,82],"cough":[48],"samples":[49],"with":[50,158],"investigate":[54],"whether":[55],"alters":[57],"frequency":[59],"content":[60],"these":[62],"signals.":[63],"Furthermore,":[64],"work":[66],"also":[67],"assesses":[68],"impact":[70],"gender":[72],"automatic":[75],"detection":[76],"COVID-19.":[78],"To":[79],"extract":[80],"deep-learnt":[81,120],"spectrograms,":[85],"we":[86],"compare":[87],"performance":[89,146],"a":[91,94],"cough-specific,":[92],"and":[93],"Resnet18":[95,155],"pre-trained":[96,156],"Convolutional":[97],"Neural":[98],"Network":[99],"(CNN).":[100],"Additionally,":[101],"our":[102,128],"approach":[103],"explores":[104],"use":[106],"contextual":[108,159],"attention,":[109,160],"so":[110],"model":[112],"can":[113],"learn":[114],"highlight":[116],"most":[118],"relevant":[119],"features":[121],"extracted":[122],"CNN.":[125],"conduct":[127],"experiments":[129],"dataset":[132],"released":[133],"Cough":[136],"Sound":[137],"Track":[138],"DICOVA":[141],"2021":[142],"Challenge.":[143],"best":[145],"test":[149],"set":[150],"obtained":[152],"using":[153],"CNN":[157],"which":[161],"scored":[162],"an":[163],"Area":[164],"Under":[165],"Curve":[167],"(AUC)":[168],"70.91":[170],"%":[171,174],"80":[173],"sensitivity.":[175]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
