{"id":"https://openalex.org/W2527993301","doi":"https://doi.org/10.1109/lsp.2016.2627029","title":"Low-Rank and Sparsity Analysis Applied to Speech Enhancement Via Online Estimated Dictionary","display_name":"Low-Rank and Sparsity Analysis Applied to Speech Enhancement Via Online Estimated Dictionary","publication_year":2016,"publication_date":"2016-11-09","ids":{"openalex":"https://openalex.org/W2527993301","doi":"https://doi.org/10.1109/lsp.2016.2627029","mag":"2527993301"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2016.2627029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2016.2627029","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1609.09231","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Pengfei Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I110378019","display_name":"Southern Illinois University Carbondale","ror":"https://ror.org/049kefs16","country_code":"US","type":"education","lineage":["https://openalex.org/I110378019","https://openalex.org/I2801502357"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Pengfei Sun","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Southern Illinois University, Carbondale, IL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Southern Illinois University, Carbondale, IL, USA","institution_ids":["https://openalex.org/I110378019"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jun Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I110378019","display_name":"Southern Illinois University Carbondale","ror":"https://ror.org/049kefs16","country_code":"US","type":"education","lineage":["https://openalex.org/I110378019","https://openalex.org/I2801502357"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Qin","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Southern Illinois University, Carbondale, IL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Southern Illinois University, Carbondale, IL, USA","institution_ids":["https://openalex.org/I110378019"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I110378019"],"apc_list":null,"apc_paid":null,"fwci":1.5282,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.84152269,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"23","issue":"12","first_page":"1862","last_page":"1866"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9678000211715698,"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":0.9678000211715698,"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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.019600000232458115,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.004399999976158142,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/speech-enhancement","display_name":"Speech enhancement","score":0.8676999807357788},{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.8499000072479248},{"id":"https://openalex.org/keywords/speech-processing","display_name":"Speech processing","score":0.5863999724388123},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.508400022983551},{"id":"https://openalex.org/keywords/robust-principal-component-analysis","display_name":"Robust principal component analysis","score":0.506600022315979},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5027999877929688},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49390000104904175},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.4661000072956085},{"id":"https://openalex.org/keywords/speech-coding","display_name":"Speech coding","score":0.4593000113964081},{"id":"https://openalex.org/keywords/linear-predictive-coding","display_name":"Linear predictive coding","score":0.388700008392334}],"concepts":[{"id":"https://openalex.org/C2776182073","wikidata":"https://www.wikidata.org/wiki/Q7575395","display_name":"Speech enhancement","level":3,"score":0.8676999807357788},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.8499000072479248},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7282000184059143},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7145000100135803},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.5863999724388123},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.508400022983551},{"id":"https://openalex.org/C2777749129","wikidata":"https://www.wikidata.org/wiki/Q17148469","display_name":"Robust principal component analysis","level":3,"score":0.506600022315979},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5027999877929688},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49390000104904175},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.4661000072956085},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4595000147819519},{"id":"https://openalex.org/C13895895","wikidata":"https://www.wikidata.org/wiki/Q3270773","display_name":"Speech coding","level":2,"score":0.4593000113964081},{"id":"https://openalex.org/C59883199","wikidata":"https://www.wikidata.org/wiki/Q1826438","display_name":"Linear predictive coding","level":3,"score":0.388700008392334},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.37709999084472656},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.3718999922275543},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.35519999265670776},{"id":"https://openalex.org/C204201278","wikidata":"https://www.wikidata.org/wiki/Q1332614","display_name":"Voice activity detection","level":3,"score":0.34540000557899475},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.33469998836517334},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.296999990940094},{"id":"https://openalex.org/C127220857","wikidata":"https://www.wikidata.org/wiki/Q2719318","display_name":"Audio signal processing","level":4,"score":0.2892000079154968},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C73208851","wikidata":"https://www.wikidata.org/wiki/Q5157303","display_name":"Computational auditory scene analysis","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27140000462532043},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.25870001316070557},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C14999030","wikidata":"https://www.wikidata.org/wiki/Q16346","display_name":"Speech synthesis","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C3020028006","wikidata":"https://www.wikidata.org/wiki/Q9158","display_name":"Electronic mail","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/lsp.2016.2627029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2016.2627029","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1609.09231","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1609.09231","pdf_url":"https://arxiv.org/pdf/1609.09231","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"pmh:oai:opensiuc.lib.siu.edu:ece_articles-1073","is_oa":false,"landing_page_url":"https://opensiuc.lib.siu.edu/ece_articles/71","pdf_url":null,"source":{"id":"https://openalex.org/S4377196411","display_name":"OpenSIUC (Southern Illinois University Carbondale)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I110378019","host_organization_name":"Southern Illinois University Carbondale","host_organization_lineage":["https://openalex.org/I110378019"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Articles","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1609.09231","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1609.09231","pdf_url":"https://arxiv.org/pdf/1609.09231","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1031527141","https://openalex.org/W1980687383","https://openalex.org/W1993962865","https://openalex.org/W2013608223","https://openalex.org/W2044083107","https://openalex.org/W2056347616","https://openalex.org/W2121729458","https://openalex.org/W2125670593","https://openalex.org/W2135284480","https://openalex.org/W2142170306","https://openalex.org/W2145760110","https://openalex.org/W2167873188","https://openalex.org/W4253928870","https://openalex.org/W6677677282","https://openalex.org/W6712292009","https://openalex.org/W6712781682","https://openalex.org/W6712867848","https://openalex.org/W6929385289"],"related_works":[],"abstract_inverted_index":{"In":[0,23],"this":[1],"letter,":[2],"we":[3],"propose":[4],"an":[5,40],"online":[6,44],"estimated":[7,63],"local":[8,62,77],"dictionary":[9,78],"based":[10],"single-channel":[11],"speech":[12,29,45,68,85,100,117,137],"enhancement":[13,138],"algorithm,":[14,26,92],"which":[15,48,94],"focuses":[16],"on":[17,47,67],"low-rank":[18,35,50],"and":[19,39,51,70,113,155],"sparse":[20],"matrix":[21,148],"decomposition.":[22],"the":[24,43,59,84,123],"proposed":[25,106,124],"a":[27,95],"noisy":[28],"spectrogram":[30],"can":[31,79],"be":[32,80],"decomposed":[33],"into":[34],"background":[36,74],"noise":[37],"components":[38,69],"activation":[41],"of":[42,61,116],"dictionary,":[46],"both":[49],"sparsity":[52],"constraints":[53],"are":[54],"imposed.":[55],"This":[56],"decomposition":[57],"takes":[58],"advantage":[60],"exemplar's":[64],"high":[65],"expressiveness":[66],"also":[71],"accommodates":[72],"nonstationary":[73],"noise.":[75],"The":[76,105,119],"obtained":[81],"through":[82],"estimating":[83],"presence":[86],"probability":[87],"(SPP)":[88],"by":[89],"applying":[90],"expectation-maximal":[91],"in":[93],"generalized":[96],"Gamma":[97],"prior":[98],"for":[99],"magnitude":[101],"spectrum":[102],"is":[103,108],"used.":[104],"algorithm":[107,125],"evaluated":[109],"using":[110],"signal-to-distortion":[111],"ratio,":[112],"perceptual":[114],"evaluation":[115],"quality.":[118],"results":[120],"show":[121],"that":[122],"achieves":[126],"significant":[127],"improvements":[128],"at":[129],"various":[130],"SNRs":[131],"when":[132],"compared":[133],"to":[134],"four":[135],"other":[136],"algorithms,":[139],"including":[140],"improved":[141],"Karhunen-Loeve":[142],"transform":[143],"approach,":[144],"SPP-based":[145],"MMSE,":[146],"nonnegative":[147],"factorization-based":[149],"robust":[150],"principal":[151],"component":[152],"analysis":[153],"(RPCA),":[154],"RPCA.":[156]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2016-10-14T00:00:00"}
