{"id":"https://openalex.org/W2770587632","doi":"https://doi.org/10.1109/mlsp.2017.8168139","title":"Leveraging deep neural networks with nonnegative representations for improved environmental sound classification","display_name":"Leveraging deep neural networks with nonnegative representations for improved environmental sound classification","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2770587632","doi":"https://doi.org/10.1109/mlsp.2017.8168139","mag":"2770587632"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2017.8168139","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2017.8168139","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5040132672","display_name":"Victor Bisot","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165912","display_name":"Laboratoire Traitement et Communication de l\u2019Information","ror":"https://ror.org/057er4c39","country_code":"FR","type":"facility","lineage":["https://openalex.org/I12356871","https://openalex.org/I205703379","https://openalex.org/I4210145102","https://openalex.org/I4210165912"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Victor Bisot","raw_affiliation_strings":["LTCI, Universit\u00e9 Paris, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LTCI, Universit\u00e9 Paris, Paris, France","institution_ids":["https://openalex.org/I4210165912"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Romain Serizel","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210121838","display_name":"Laboratoire Lorrain de Recherche en Informatique et ses Applications","ror":"https://ror.org/02vnf0c38","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I277688954","https://openalex.org/I4210107720","https://openalex.org/I4210121838","https://openalex.org/I4210159245","https://openalex.org/I90183372"]},{"id":"https://openalex.org/I4210127166","display_name":"Centre Inria de l'Universit\u00e9 de Lorraine","ror":"https://ror.org/03fcjvn64","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1326498283","https://openalex.org/I4210127166"]},{"id":"https://openalex.org/I90183372","display_name":"Universit\u00e9 de Lorraine","ror":"https://ror.org/04vfs2w97","country_code":"FR","type":"education","lineage":["https://openalex.org/I90183372"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Romain Serizel","raw_affiliation_strings":["CNRS, LORIA, Vandceuvre-l\u00e9s-Nancy, France","Inria, Villers-l\u00e9s-Nancy, France","Universit\u00e9 de Lorraine, LORIA, Vand\u00e6uvre-l\u00e9s-Nancy, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CNRS, LORIA, Vandceuvre-l\u00e9s-Nancy, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210121838"]},{"raw_affiliation_string":"Inria, Villers-l\u00e9s-Nancy, France","institution_ids":["https://openalex.org/I4210127166"]},{"raw_affiliation_string":"Universit\u00e9 de Lorraine, LORIA, Vand\u00e6uvre-l\u00e9s-Nancy, France","institution_ids":["https://openalex.org/I4210121838","https://openalex.org/I90183372"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060031161","display_name":"Slim Essid","orcid":"https://orcid.org/0000-0002-0028-327X"},"institutions":[{"id":"https://openalex.org/I4210165912","display_name":"Laboratoire Traitement et Communication de l\u2019Information","ror":"https://ror.org/057er4c39","country_code":"FR","type":"facility","lineage":["https://openalex.org/I12356871","https://openalex.org/I205703379","https://openalex.org/I4210145102","https://openalex.org/I4210165912"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Slim Essid","raw_affiliation_strings":["LTCI, Universit\u00e9 Paris, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LTCI, Universit\u00e9 Paris, Paris, France","institution_ids":["https://openalex.org/I4210165912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055423112","display_name":"Ga\u00ebl Richard","orcid":"https://orcid.org/0000-0002-4960-0010"},"institutions":[{"id":"https://openalex.org/I4210165912","display_name":"Laboratoire Traitement et Communication de l\u2019Information","ror":"https://ror.org/057er4c39","country_code":"FR","type":"facility","lineage":["https://openalex.org/I12356871","https://openalex.org/I205703379","https://openalex.org/I4210145102","https://openalex.org/I4210165912"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Gael Richard","raw_affiliation_strings":["LTCI, Universit\u00e9 Paris, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LTCI, Universit\u00e9 Paris, Paris, France","institution_ids":["https://openalex.org/I4210165912"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9990000128746033,"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/T11665","display_name":"Animal Vocal Communication and Behavior","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/1309","display_name":"Developmental Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.7954829931259155},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7805800437927246},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.7637532949447632},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7088512778282166},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6315590143203735},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.62518310546875},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5582338571548462},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5450265407562256},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5272026658058167},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5099157094955444},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3710384964942932},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.3332276940345764},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.33277419209480286}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.7954829931259155},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7805800437927246},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.7637532949447632},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7088512778282166},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6315590143203735},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.62518310546875},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5582338571548462},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5450265407562256},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5272026658058167},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5099157094955444},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3710384964942932},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.3332276940345764},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.33277419209480286},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/mlsp.2017.8168139","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2017.8168139","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-01576857v1","is_oa":false,"landing_page_url":"https://hal.science/hal-01576857","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE International Workshop on Machine Learning for Signal Processing MLSP, Sep 2017, Tokyo, Japan","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.47999998927116394,"display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1511170870","https://openalex.org/W1555814299","https://openalex.org/W1902027874","https://openalex.org/W1904365287","https://openalex.org/W2095147901","https://openalex.org/W2103235956","https://openalex.org/W2105622288","https://openalex.org/W2110798204","https://openalex.org/W2136922672","https://openalex.org/W2137343183","https://openalex.org/W2137561966","https://openalex.org/W2163112044","https://openalex.org/W2212106655","https://openalex.org/W2295300449","https://openalex.org/W2341412280","https://openalex.org/W2509065397","https://openalex.org/W2511956680","https://openalex.org/W2517876061","https://openalex.org/W2519994964","https://openalex.org/W2529337537","https://openalex.org/W2546302380","https://openalex.org/W2553108847","https://openalex.org/W2566935005","https://openalex.org/W2593116425","https://openalex.org/W2604352297","https://openalex.org/W2619623002","https://openalex.org/W2914312680","https://openalex.org/W2963500860","https://openalex.org/W3092213245","https://openalex.org/W3098357269","https://openalex.org/W3103385400","https://openalex.org/W6675803605","https://openalex.org/W6676481782","https://openalex.org/W6696886941","https://openalex.org/W6784489076","https://openalex.org/W6980901144"],"related_works":["https://openalex.org/W2551137307","https://openalex.org/W2113526703","https://openalex.org/W2396837968","https://openalex.org/W2127243424","https://openalex.org/W4390394189","https://openalex.org/W2037504162","https://openalex.org/W2539013788","https://openalex.org/W2123043102","https://openalex.org/W2098101267","https://openalex.org/W2577807713"],"abstract_inverted_index":{"This":[0,91],"paper":[1],"introduces":[2],"the":[3,33,133,137],"use":[4],"of":[5],"representations":[6,38,86,123],"based":[7],"on":[8,32,74,121,124],"nonnegative":[9],"matrix":[10],"factorization":[11],"(NMF)":[12],"to":[13,20,35,82,98],"train":[14],"deep":[15,54,88],"neural":[16,89,118],"networks":[17,109,119],"with":[18,95,102],"applications":[19],"environmental":[21],"sound":[22,28,70],"classification.":[23,113],"Deep":[24],"learning":[25,50],"systems":[26,105,116,135],"for":[27,64,87,110],"classification":[29,128],"usually":[30],"rely":[31,73],"network":[34],"learn":[36,83],"meaningful":[37],"from":[39,136],"spectrograms":[40],"or":[41],"hand-crafted":[42],"features.":[43],"Instead,":[44],"we":[45],"introduce":[46],"a":[47],"NMF-based":[48],"feature":[49],"stage":[51],"before":[52],"training":[53],"networks,":[55],"whose":[56],"usefulness":[57],"is":[58],"highlighted":[59],"in":[60],"this":[61],"paper,":[62],"especially":[63],"multi-source":[65],"acoustic":[66,111,126],"environments":[67],"such":[68,106],"as":[69,107,130,132],"scenes.":[71],"We":[72],"two":[75,125],"established":[76],"unsupervised":[77],"and":[78],"supervised":[79],"NMF":[80],"techniques":[81],"better":[84],"input":[85],"networks.":[90],"will":[92],"allow":[93],"us,":[94],"simple":[96],"architectures,":[97],"reach":[99],"competitive":[100],"performance":[101],"more":[103],"complex":[104],"convolutional":[108],"scene":[112,127],"The":[114],"proposed":[115],"outperform":[117],"trained":[120],"time-frequency":[122],"datasets":[129],"well":[131],"best":[134],"2016":[138],"DCASE":[139],"challenge.":[140]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}