{"id":"https://openalex.org/W3040156878","doi":"https://doi.org/10.1109/access.2020.3007199","title":"Feature Extraction Based on the Non-Negative Matrix Factorization of Convolutional Neural Networks for Monitoring Domestic Activity With Acoustic Signals","display_name":"Feature Extraction Based on the Non-Negative Matrix Factorization of Convolutional Neural Networks for Monitoring Domestic Activity With Acoustic Signals","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3040156878","doi":"https://doi.org/10.1109/access.2020.3007199","mag":"3040156878"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3007199","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3007199","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09133398.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09133398.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062101749","display_name":"Seokjin Lee","orcid":"https://orcid.org/0000-0001-8220-192X"},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Seokjin Lee","raw_affiliation_strings":["School of Electronics Engineering, Kyungpook National University, Daegu, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-8220-192X","affiliations":[{"raw_affiliation_string":"School of Electronics Engineering, Kyungpook National University, Daegu, South Korea","institution_ids":["https://openalex.org/I31419693"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100629514","display_name":"Hee\u2010Suk Pang","orcid":"https://orcid.org/0000-0002-7108-8957"},"institutions":[{"id":"https://openalex.org/I28777354","display_name":"Sejong University","ror":"https://ror.org/00aft1q37","country_code":"KR","type":"education","lineage":["https://openalex.org/I28777354"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hee-Suk Pang","raw_affiliation_strings":["Department of Electrical Engineering, Sejong University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Sejong University, Seoul, South Korea","institution_ids":["https://openalex.org/I28777354"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5062101749"],"corresponding_institution_ids":["https://openalex.org/I31419693"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.8232,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.85807589,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"8","issue":null,"first_page":"122384","last_page":"122395"},"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":0.9997000098228455,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9883999824523926,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.8932158350944519},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7745104432106018},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7422190308570862},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.69832843542099},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6291115283966064},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5836017727851868},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.5761390924453735},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5565972924232483},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.45711660385131836},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.45696184039115906},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4331434667110443},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.38008081912994385}],"concepts":[{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.8932158350944519},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7745104432106018},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7422190308570862},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.69832843542099},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6291115283966064},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5836017727851868},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.5761390924453735},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5565972924232483},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.45711660385131836},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.45696184039115906},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4331434667110443},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.38008081912994385},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3007199","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3007199","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09133398.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:67b6d0d5c2a247ebbfa8d5bfd2bfee36","is_oa":true,"landing_page_url":"https://doaj.org/article/67b6d0d5c2a247ebbfa8d5bfd2bfee36","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 122384-122395 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3007199","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3007199","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09133398.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2391801618","display_name":null,"funder_award_id":"2016-0-00564","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G5938352060","display_name":null,"funder_award_id":"2016-0-00564","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"}],"funders":[{"id":"https://openalex.org/F4320324891","display_name":"Iran Telecommunication Research Center","ror":"https://ror.org/01a3g2z22"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3040156878.pdf","grobid_xml":"https://content.openalex.org/works/W3040156878.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W1246381107","https://openalex.org/W1480376833","https://openalex.org/W1511170870","https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1902027874","https://openalex.org/W1959749536","https://openalex.org/W1973746598","https://openalex.org/W1975678038","https://openalex.org/W1978274651","https://openalex.org/W1997528538","https://openalex.org/W2051434435","https://openalex.org/W2076608692","https://openalex.org/W2104298926","https://openalex.org/W2105143211","https://openalex.org/W2115109087","https://openalex.org/W2128728535","https://openalex.org/W2129638017","https://openalex.org/W2135029798","https://openalex.org/W2137561966","https://openalex.org/W2150415460","https://openalex.org/W2158291955","https://openalex.org/W2161755617","https://openalex.org/W2200864288","https://openalex.org/W2326699523","https://openalex.org/W2403172465","https://openalex.org/W2519994964","https://openalex.org/W2526050071","https://openalex.org/W2566935005","https://openalex.org/W2618553051","https://openalex.org/W2619623002","https://openalex.org/W2735072998","https://openalex.org/W2888939682","https://openalex.org/W2942340836","https://openalex.org/W2957789971","https://openalex.org/W2963500860","https://openalex.org/W2963812188","https://openalex.org/W2972470942","https://openalex.org/W2982500554","https://openalex.org/W2982686237","https://openalex.org/W3011736257","https://openalex.org/W3016107658","https://openalex.org/W3092213245","https://openalex.org/W4295723153","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6680012447","https://openalex.org/W6752516136","https://openalex.org/W6765212446","https://openalex.org/W6770667839","https://openalex.org/W6784489076"],"related_works":["https://openalex.org/W2127243424","https://openalex.org/W4390394189","https://openalex.org/W2037504162","https://openalex.org/W2539013788","https://openalex.org/W2792706544","https://openalex.org/W1568451138","https://openalex.org/W2156699640","https://openalex.org/W2045265907","https://openalex.org/W2972997031","https://openalex.org/W2075222291"],"abstract_inverted_index":{"In":[0,123,148],"this":[1,125],"paper,":[2],"a":[3,128],"feature":[4,47,66,111,135,161,226],"extraction":[5,48,67,227],"method":[6,68,80,131,144,228],"is":[7,70,106,137,230],"proposed":[8,79,110,134,153,159,200],"based":[9,231],"on":[10,190,232],"the":[11,27,30,73,83,93,98,103,109,114,119,133,140,152,158,163,171,199,215,223],"non-negative":[12],"matrix":[13,86,117],"factorization":[14],"(NMF)":[15],"for":[16,18,87,132,145],"classifiers":[17,28],"monitoring":[19],"domestic":[20],"activities":[21],"with":[22,57,118,222],"acoustic":[23,31,104],"signals.":[24],"Most":[25],"of":[26,29,157,175,186,207,218],"signals":[32],"use":[33],"data-independent":[34],"spectral":[35],"features":[36,55],"(e.g.,":[37],"log-Mel":[38],"spectrum":[39],"and":[40,90,142,162,173,178,210,235],"Mel-frequency":[41],"cepstral":[42],"coefficients).":[43],"Recently,":[44],"some":[45,166],"novel":[46],"methods":[49],"have":[50],"been":[51],"researched,":[52],"including":[53],"convolution-NMF-based":[54],"combined":[56],"K-means":[58,236],"clustering.":[59,237],"This":[60],"study":[61,126],"proposes":[62,127],"an":[63],"enhanced":[64,214],"NMF-based":[65,74,160,225],"that":[69,136,198,206,211,229],"inspired":[71,138],"by":[72,112,139],"noise":[75],"reduction":[76],"algorithm.":[77],"The":[78,195],"independently":[81],"estimates":[82],"frequency":[84,100,121],"basis":[85,94,116],"each":[88],"class,":[89],"then":[91],"cascades":[92],"matrices":[95],"to":[96,108,150,205],"form":[97],"entire":[99],"bases,":[101],"where":[102],"signal":[105],"transformed":[107],"estimating":[113],"temporal":[115],"trained":[120],"bases.":[122],"addition,":[124],"data":[129],"augmentation":[130],"\u201cmix":[141],"shuffle\u201d":[143],"audio":[146],"waveforms.":[147],"order":[149],"evaluate":[151],"system,":[154],"which":[155],"consists":[156],"convolutional-neural-network-based":[164],"classifier,":[165],"evaluations":[167],"were":[168],"performed":[169],"using":[170],"Detection":[172],"Classification":[174],"Acoustic":[176],"Scenes":[177],"Events":[179],"(DCASE)":[180],"2018":[181],"Task":[182],"5":[183],"-":[184,193],"Monitoring":[185],"Domestic":[187],"Activities":[188],"Based":[189],"Multi-channel":[191],"Acoustics":[192],"Database.":[194],"results":[196],"showed":[197],"system":[201],"has":[202,213],"comparable":[203],"performance":[204,217],"state-of-the-art":[208],"algorithms":[209],"it":[212],"F1-score":[216],"6%-12%":[219],"in":[220],"comparison":[221],"conventional":[224],"convolutional":[233],"NMF":[234]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":3}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}