{"id":"https://openalex.org/W2902459807","doi":"https://doi.org/10.23919/eusipco.2018.8553123","title":"Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering","display_name":"Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2902459807","doi":"https://doi.org/10.23919/eusipco.2018.8553123","mag":"2902459807"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco.2018.8553123","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2018.8553123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 26th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://vbn.aau.dk/ws/files/289318599/PID5424137.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007282822","display_name":"Mahmoud Fakhry","orcid":"https://orcid.org/0000-0002-2170-2998"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Mahmoud Fakhry","raw_affiliation_strings":["Audio Analysis Lab, Aalborg University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Audio Analysis Lab, Aalborg University","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019002740","display_name":"Amir Hossein Poorjam","orcid":"https://orcid.org/0000-0002-6882-4618"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Amir Hossein Poorjam","raw_affiliation_strings":["Audio Analysis Lab, Aalborg University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Audio Analysis Lab, Aalborg University","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026658144","display_name":"Mads Gr\u00e6sb\u00f8ll Christensen","orcid":"https://orcid.org/0000-0003-3586-7969"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Mads Graesboll Christensen","raw_affiliation_strings":["Audio Analysis Lab, Aalborg University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Audio Analysis Lab, Aalborg University","institution_ids":["https://openalex.org/I891191580"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I891191580"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"16","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech 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/T10860","display_name":"Speech 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/T11447","display_name":"Blind Source Separation Techniques","score":0.9973000288009644,"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":0.9948999881744385,"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":0.8686667084693909},{"id":"https://openalex.org/keywords/wiener-filter","display_name":"Wiener filter","score":0.6876398324966431},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.684258222579956},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6604904532432556},{"id":"https://openalex.org/keywords/speech-enhancement","display_name":"Speech enhancement","score":0.6532074809074402},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.6376149654388428},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.621372401714325},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5945795178413391},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5864712595939636},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.49300047755241394},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.47436079382896423},{"id":"https://openalex.org/keywords/intelligibility","display_name":"Intelligibility (philosophy)","score":0.4722000062465668},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4463311433792114},{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.43512338399887085},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4338954985141754},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.33210286498069763},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.250978946685791}],"concepts":[{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.8686667084693909},{"id":"https://openalex.org/C18537770","wikidata":"https://www.wikidata.org/wiki/Q25523","display_name":"Wiener filter","level":2,"score":0.6876398324966431},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.684258222579956},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6604904532432556},{"id":"https://openalex.org/C2776182073","wikidata":"https://www.wikidata.org/wiki/Q7575395","display_name":"Speech enhancement","level":3,"score":0.6532074809074402},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.6376149654388428},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.621372401714325},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5945795178413391},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5864712595939636},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.49300047755241394},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.47436079382896423},{"id":"https://openalex.org/C60048801","wikidata":"https://www.wikidata.org/wiki/Q1433889","display_name":"Intelligibility (philosophy)","level":2,"score":0.4722000062465668},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4463311433792114},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.43512338399887085},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4338954985141754},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.33210286498069763},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.250978946685791},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/eusipco.2018.8553123","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2018.8553123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 26th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/44738e4d-9fec-4a38-a508-c1fb8031a7d0","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/44738e4d-9fec-4a38-a508-c1fb8031a7d0","pdf_url":"https://vbn.aau.dk/ws/files/289318599/PID5424137.pdf","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"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":"Fakhry, M, Poorjam, A H & Christensen, M G 2018, Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering. in 26th European Signal Processing Conference (EUSIPCO)., 8553123, IEEE (Institute of Electrical and Electronics Engineers), Proceedings of the European Signal Processing Conference, 26th European Signal Processing Conference (EUSIPCO 2018), Rome, Italy, 03/09/2018. https://doi.org/10.23919/EUSIPCO.2018.8553123","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:publications/44738e4d-9fec-4a38-a508-c1fb8031a7d0","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/44738e4d-9fec-4a38-a508-c1fb8031a7d0","pdf_url":"https://vbn.aau.dk/ws/files/289318599/PID5424137.pdf","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"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":"Fakhry, M, Poorjam, A H & Christensen, M G 2018, Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering. in 26th European Signal Processing Conference (EUSIPCO)., 8553123, IEEE (Institute of Electrical and Electronics Engineers), Proceedings of the European Signal Processing Conference, 26th European Signal Processing Conference (EUSIPCO 2018), Rome, Italy, 03/09/2018. https://doi.org/10.23919/EUSIPCO.2018.8553123","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2902459807.pdf","grobid_xml":"https://content.openalex.org/works/W2902459807.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W165956390","https://openalex.org/W1493163583","https://openalex.org/W1504438288","https://openalex.org/W1552314771","https://openalex.org/W1902027874","https://openalex.org/W1980183422","https://openalex.org/W2046233597","https://openalex.org/W2048687561","https://openalex.org/W2051428568","https://openalex.org/W2114508388","https://openalex.org/W2115594062","https://openalex.org/W2119821739","https://openalex.org/W2121894251","https://openalex.org/W2140828385","https://openalex.org/W2144561273","https://openalex.org/W2166333654","https://openalex.org/W2235103357","https://openalex.org/W2254223938","https://openalex.org/W2404400936","https://openalex.org/W2607108972","https://openalex.org/W2748130992","https://openalex.org/W2802812753","https://openalex.org/W2952929324","https://openalex.org/W4239510810","https://openalex.org/W4253928870","https://openalex.org/W6684477786"],"related_works":["https://openalex.org/W2018086531","https://openalex.org/W1980297060","https://openalex.org/W2387604097","https://openalex.org/W4385672897","https://openalex.org/W2106793170","https://openalex.org/W2373675101","https://openalex.org/W1577562165","https://openalex.org/W106160982","https://openalex.org/W2919389044","https://openalex.org/W334008168"],"abstract_inverted_index":{"Supervised":[0],"non-negative":[1],"matrix":[2],"factorization":[3],"(NMF)":[4],"is":[5,32,74,86],"effective":[6],"in":[7,44,57,96],"speech":[8,15,38,132],"enhancement":[9,21],"through":[10,67],"training":[11,131],"spectral":[12],"models":[13,26],"of":[14,64,127,130,144],"and":[16,40,82,115,133],"noise":[17,42,134],"signals.":[18],"However,":[19],"the":[20,25,65,68,77,83,90,97,101,113,123,128,137,142,145,150],"quality":[22],"reduces":[23],"when":[24],"are":[27,94],"trained":[28,69,102,121],"on":[29,122],"data":[30],"that":[31],"not":[33],"highly":[34],"relevant":[35],"to":[36,53,59,75],"a":[37,41,45,55,117],"signal":[39,43,85],"noisy":[46,78],"observation.":[47],"In":[48],"this":[49],"paper,":[50],"we":[51],"propose":[52],"train":[54],"classifier":[56],"order":[58],"overcome":[60],"such":[61],"poor":[62],"characterization":[63],"signals":[66,135],"models.":[70],"The":[71,139],"main":[72],"idea":[73],"decompose":[76],"observation":[79],"into":[80],"parts":[81,129],"enhanced":[84],"reconstructed":[87],"by":[88,109],"combining":[89],"less-corrupted":[91],"ones":[92],"which":[93],"identified":[95],"cepstral":[98,125],"domain":[99],"using":[100],"classifier.":[103],"We":[104],"apply":[105],"unsupervised":[106],"NMF":[107],"followed":[108],"Wiener":[110],"filtering":[111],"for":[112,136],"decomposition,":[114],"use":[116],"support":[118],"vector":[119],"machine":[120],"mel-frequency":[124],"coefficients":[126],"classification.":[138],"results":[140],"show":[141],"effectiveness":[143],"proposed":[146],"method":[147],"compared":[148],"with":[149],"supervised":[151],"NMF.":[152]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}