{"id":"https://openalex.org/W4390693180","doi":"https://doi.org/10.1109/icecs58634.2023.10382751","title":"AusculNET: A Deep Learning framework for Adventitious Lung Sounds Classification","display_name":"AusculNET: A Deep Learning framework for Adventitious Lung Sounds Classification","publication_year":2023,"publication_date":"2023-12-04","ids":{"openalex":"https://openalex.org/W4390693180","doi":"https://doi.org/10.1109/icecs58634.2023.10382751"},"language":"en","primary_location":{"id":"doi:10.1109/icecs58634.2023.10382751","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecs58634.2023.10382751","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS)","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/A5031079897","display_name":"Charalampos Papadakis","orcid":"https://orcid.org/0000-0002-5727-274X"},"institutions":[{"id":"https://openalex.org/I4210114974","display_name":"IMEC","ror":"https://ror.org/02kcbn207","country_code":"BE","type":"nonprofit","lineage":["https://openalex.org/I4210114974"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Charalampos Papadakis","raw_affiliation_strings":["IMEC,LTE,Leuven,Belgium","LTE, IMEC, Leuven, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IMEC,LTE,Leuven,Belgium","institution_ids":["https://openalex.org/I4210114974"]},{"raw_affiliation_string":"LTE, IMEC, Leuven, Belgium","institution_ids":["https://openalex.org/I4210114974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049999991","display_name":"Leandro M. G. Rocha","orcid":"https://orcid.org/0000-0003-2883-2768"},"institutions":[{"id":"https://openalex.org/I4210114974","display_name":"IMEC","ror":"https://ror.org/02kcbn207","country_code":"BE","type":"nonprofit","lineage":["https://openalex.org/I4210114974"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Leandro M. Giacomini Rocha","raw_affiliation_strings":["IMEC,LTE,Leuven,Belgium","LTE, IMEC, Leuven, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IMEC,LTE,Leuven,Belgium","institution_ids":["https://openalex.org/I4210114974"]},{"raw_affiliation_string":"LTE, IMEC, Leuven, Belgium","institution_ids":["https://openalex.org/I4210114974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033235745","display_name":"Francky Catthoor","orcid":"https://orcid.org/0000-0002-3599-8515"},"institutions":[{"id":"https://openalex.org/I4210114974","display_name":"IMEC","ror":"https://ror.org/02kcbn207","country_code":"BE","type":"nonprofit","lineage":["https://openalex.org/I4210114974"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Francky Catthoor","raw_affiliation_strings":["IMEC,CSO,Leuven,Belgium","CSO, IMEC, Leuven, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IMEC,CSO,Leuven,Belgium","institution_ids":["https://openalex.org/I4210114974"]},{"raw_affiliation_string":"CSO, IMEC, Leuven, Belgium","institution_ids":["https://openalex.org/I4210114974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013502488","display_name":"Nick Van Helleputte","orcid":"https://orcid.org/0000-0002-7511-1923"},"institutions":[{"id":"https://openalex.org/I4210114974","display_name":"IMEC","ror":"https://ror.org/02kcbn207","country_code":"BE","type":"nonprofit","lineage":["https://openalex.org/I4210114974"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Nick Van Helleputte","raw_affiliation_strings":["IMEC,LST,Leuven,Belgium","LST, IMEC, Leuven, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IMEC,LST,Leuven,Belgium","institution_ids":["https://openalex.org/I4210114974"]},{"raw_affiliation_string":"LST, IMEC, Leuven, Belgium","institution_ids":["https://openalex.org/I4210114974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060076901","display_name":"Dwaipayan Biswas","orcid":"https://orcid.org/0000-0001-7912-3692"},"institutions":[{"id":"https://openalex.org/I4210114974","display_name":"IMEC","ror":"https://ror.org/02kcbn207","country_code":"BE","type":"nonprofit","lineage":["https://openalex.org/I4210114974"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Dwaipayan Biswas","raw_affiliation_strings":["IMEC,LTE,Leuven,Belgium","LTE, IMEC, Leuven, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IMEC,LTE,Leuven,Belgium","institution_ids":["https://openalex.org/I4210114974"]},{"raw_affiliation_string":"LTE, IMEC, Leuven, Belgium","institution_ids":["https://openalex.org/I4210114974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210114974"],"apc_list":null,"apc_paid":null,"fwci":3.9243,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.93972272,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T12418","display_name":"Respiratory and Cough-Related Research","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T11309","display_name":"Music and Audio Processing","score":0.9684000015258789,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7383884191513062},{"id":"https://openalex.org/keywords/stethoscope","display_name":"Stethoscope","score":0.6978408098220825},{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.6385955810546875},{"id":"https://openalex.org/keywords/auscultation","display_name":"Auscultation","score":0.6354822516441345},{"id":"https://openalex.org/keywords/wheeze","display_name":"Wheeze","score":0.6151842474937439},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6050622463226318},{"id":"https://openalex.org/keywords/respiratory-sounds","display_name":"Respiratory sounds","score":0.5794366598129272},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5605157613754272},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5129813551902771},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5100832581520081},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47698718309402466},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4677830934524536},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.4361454248428345},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3843322694301605},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3785315752029419},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.15793690085411072},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.10669860243797302}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7383884191513062},{"id":"https://openalex.org/C2779055095","wikidata":"https://www.wikidata.org/wiki/Q162339","display_name":"Stethoscope","level":2,"score":0.6978408098220825},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.6385955810546875},{"id":"https://openalex.org/C2777324038","wikidata":"https://www.wikidata.org/wiki/Q779054","display_name":"Auscultation","level":2,"score":0.6354822516441345},{"id":"https://openalex.org/C2778338976","wikidata":"https://www.wikidata.org/wiki/Q517104","display_name":"Wheeze","level":3,"score":0.6151842474937439},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6050622463226318},{"id":"https://openalex.org/C2777402568","wikidata":"https://www.wikidata.org/wiki/Q779038","display_name":"Respiratory sounds","level":3,"score":0.5794366598129272},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5605157613754272},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5129813551902771},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5100832581520081},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47698718309402466},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4677830934524536},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.4361454248428345},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3843322694301605},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3785315752029419},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.15793690085411072},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.10669860243797302},{"id":"https://openalex.org/C2776042228","wikidata":"https://www.wikidata.org/wiki/Q35869","display_name":"Asthma","level":2,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icecs58634.2023.10382751","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecs58634.2023.10382751","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2140527594","https://openalex.org/W2537763288","https://openalex.org/W2768137799","https://openalex.org/W2782469814","https://openalex.org/W2964439653","https://openalex.org/W3021778999","https://openalex.org/W3082250113","https://openalex.org/W3095616795","https://openalex.org/W3097359093","https://openalex.org/W3116715673","https://openalex.org/W3174777244","https://openalex.org/W3188566655","https://openalex.org/W4225915947","https://openalex.org/W4226150995"],"related_works":["https://openalex.org/W3092883565","https://openalex.org/W4375852858","https://openalex.org/W4312368572","https://openalex.org/W2952454465","https://openalex.org/W2071013208","https://openalex.org/W1995001437","https://openalex.org/W2797852240","https://openalex.org/W4312229671","https://openalex.org/W4380631504","https://openalex.org/W2615070281"],"abstract_inverted_index":{"Auscultation":[0],"with":[1,67,99,128,208,211],"a":[2,55],"stethoscope":[3,53],"is":[4,54],"the":[5,35,39,71,143,147,173,196],"most":[6],"conventional":[7],"means":[8,45],"of":[9,41,70,154,188,205],"examining":[10],"and":[11,17,23,80,119,131,165,190,198,222,231],"obtaining":[12],"first":[13,56],"insights":[14],"into":[15],"cardiovascular":[16],"pulmonary":[18,48],"disorders.":[19],"Although":[20],"being":[21],"noninvasive":[22],"inexpensive,":[24],"requires":[25],"access":[26],"to":[27,87,91,141,201],"medical":[28],"professionals":[29],"for":[30,46,195],"their":[31],"optimal":[32],"usage.":[33],"With":[34],"recent":[36],"COVID-19":[37],"episode,":[38],"importance":[40],"having":[42,180],"an":[43,105,186,191,212],"in-house":[44],"quick":[47],"assessment":[49],"has":[50],"grown.":[51],"Digital":[52],"step":[57],"towards":[58,214],"that":[59],"but":[60],"would":[61],"be":[62,85,97],"more":[63],"potent":[64],"if":[65],"combined":[66,86],"on-device/online":[68],"analysis":[69,109],"sensed":[72],"acoustic":[73,77,101],"signals.":[74],"Advancements":[75],"in":[76,126,146,228,233],"signal":[78,129],"processing":[79],"deep":[81],"learning":[82],"models":[83],"can":[84],"propose":[88,104],"new":[89],"solutions":[90],"lung":[92,107,148,156],"sound":[93,108,149],"classification":[94],"which":[95],"could":[96],"integrated":[98],"pervasive":[100],"sensors.":[102],"We":[103,184],"automated":[106],"framework":[110],"AusculNET,":[111],"combining":[112],"three":[113],"convolution":[114],"neural":[115],"network":[116,224],"(CNN)":[117],"layers":[118],"one":[120],"long-short":[121],"term":[122],"memory":[123],"(LSTM)":[124],"layer":[125],"conjunction":[127],"preprocessing":[130],"log-Mel":[132],"spectrogram":[133],"from":[134],"each":[135],"candidate":[136],"breath":[137,182],"cycle.":[138],"AusculNET":[139],"helps":[140],"decode":[142],"transient":[144],"information":[145],"waveform,":[150],"classifying":[151],"four":[152],"classes":[153],"adventitious":[155],"sounds":[157],"-":[158],"normal,":[159],"crackle,":[160],"wheeze,":[161],"or":[162],"both":[163],"(crackle":[164],"wheeze).":[166],"The":[167],"explorations":[168],"are":[169],"carried":[170],"out":[171],"on":[172],"ICBHI":[174,192],"2017":[175],"Challenge":[176],"Respiratory":[177],"Sound":[178],"Database,":[179],"6898":[181],"cycles.":[183],"achieve":[185],"accuracy":[187,239],"58%":[189],"score":[193],"(accounting":[194],"sensitivity":[197],"specificity":[199],"due":[200],"unequal":[202],"class":[203],"distribution)":[204],"55%,":[206],"comparable":[207],"state-of-the-art.":[209],"Furthermore,":[210],"eye":[213],"real-time":[215],"implementation,":[216],"our":[217],"8-bits":[218],"quantized":[219],"(both":[220],"weights":[221],"activation)":[223],"achieves":[225],"significant":[226],"reduction":[227],"model":[229],"parameters":[230],"improvement":[232],"overall":[234],"inference":[235],"times,":[236],"incurring":[237],"negligible":[238],"loss.":[240]},"counts_by_year":[{"year":2025,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}