{"id":"https://openalex.org/W4312266613","doi":"https://doi.org/10.1145/3556384.3556404","title":"A Unifying Framework for Blind Source Separation Algorithms Based on Generalized Eigen-value Decomposition","display_name":"A Unifying Framework for Blind Source Separation Algorithms Based on Generalized Eigen-value Decomposition","publication_year":2022,"publication_date":"2022-08-04","ids":{"openalex":"https://openalex.org/W4312266613","doi":"https://doi.org/10.1145/3556384.3556404"},"language":"en","primary_location":{"id":"doi:10.1145/3556384.3556404","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3556384.3556404","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 5th International Conference on Signal Processing and Machine Learning","raw_type":"proceedings-article"},"type":"article","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/A5052763174","display_name":"Changli Li","orcid":"https://orcid.org/0000-0003-1577-0748"},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Changli Li","raw_affiliation_strings":["School of Artificial Intelligence, Nanjing University of Information Science and Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Nanjing University of Information Science and Technology, China","institution_ids":["https://openalex.org/I200845125"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5052763174"],"corresponding_institution_ids":["https://openalex.org/I200845125"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14967064,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"124","last_page":"131"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","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/T11447","display_name":"Blind Source Separation Techniques","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/T10860","display_name":"Speech and Audio Processing","score":0.9918000102043152,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9842000007629395,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/blind-signal-separation","display_name":"Blind signal separation","score":0.7788780331611633},{"id":"https://openalex.org/keywords/separation","display_name":"Separation (statistics)","score":0.6068079471588135},{"id":"https://openalex.org/keywords/source-separation","display_name":"Source separation","score":0.6039733290672302},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5897265076637268},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5458387136459351},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5311440825462341},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.5065175294876099},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.4600922465324402},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.411366730928421},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.2410217523574829},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10384601354598999},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10017135739326477},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.09578847885131836}],"concepts":[{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.7788780331611633},{"id":"https://openalex.org/C2776061190","wikidata":"https://www.wikidata.org/wiki/Q7451805","display_name":"Separation (statistics)","level":2,"score":0.6068079471588135},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.6039733290672302},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5897265076637268},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5458387136459351},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5311440825462341},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.5065175294876099},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.4600922465324402},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.411366730928421},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.2410217523574829},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10384601354598999},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10017135739326477},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.09578847885131836},{"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/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3556384.3556404","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3556384.3556404","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 5th International Conference on Signal Processing and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G8268901501","display_name":"\u975e\u6b63\u4ea4\u8054\u5408\u5bf9\u89d2\u5316\u7406\u8bba\u53ca\u5176\u5728\u76f2\u6e90\u5206\u79bb\u4e2d\u7684\u5e94\u7528\u7814\u7a76","funder_award_id":"61871174","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W38355995","https://openalex.org/W151604921","https://openalex.org/W1598785144","https://openalex.org/W1602659231","https://openalex.org/W1966688697","https://openalex.org/W1978280156","https://openalex.org/W1981943382","https://openalex.org/W2011727610","https://openalex.org/W2022052418","https://openalex.org/W2051710799","https://openalex.org/W2059683782","https://openalex.org/W2091353418","https://openalex.org/W2099476272","https://openalex.org/W2099855488","https://openalex.org/W2116297349","https://openalex.org/W2116932888","https://openalex.org/W2123365490","https://openalex.org/W2124757684","https://openalex.org/W2128924185","https://openalex.org/W2142638745","https://openalex.org/W2152355934","https://openalex.org/W2156550040","https://openalex.org/W2157050350","https://openalex.org/W2158239398","https://openalex.org/W2165688609","https://openalex.org/W2166637868","https://openalex.org/W2170787621","https://openalex.org/W2394130806","https://openalex.org/W2507652549","https://openalex.org/W2883568091","https://openalex.org/W2900051367","https://openalex.org/W2909963827","https://openalex.org/W2910874004","https://openalex.org/W2913806294","https://openalex.org/W2917231889","https://openalex.org/W2919806192","https://openalex.org/W2944382864","https://openalex.org/W2950628866"],"related_works":["https://openalex.org/W1509813908","https://openalex.org/W2031820693","https://openalex.org/W2782904003","https://openalex.org/W3024816962","https://openalex.org/W4226434912","https://openalex.org/W2118633810","https://openalex.org/W973023320","https://openalex.org/W2150953077","https://openalex.org/W2002598339","https://openalex.org/W1910172735"],"abstract_inverted_index":{"In":[0,112],"this":[1,107,113],"paper,":[2,114],"we":[3,115,167],"present":[4],"a":[5,32,61,73,86,119],"unifying":[6,120,165,204],"framework":[7,121,205],"for":[8,24,49,122,160,202],"linear":[9],"blind":[10],"source":[11,136,192],"separation":[12,96],"(BSS)":[13],"via":[14,22,47,124],"generalized":[15,89,171],"eigen-value":[16],"decomposition":[17],"(GEVD).":[18],"The":[19,51,76],"derived":[20],"algorithms":[21,46,105,159],"GEVD":[23,33,48,59,175],"BSS":[25,123],"turn":[26],"the":[27,58,150,153,163,174,178,181,190],"underlying":[28],"optimization":[29],"problem":[30,176],"into":[31],"problem,":[34],"thus":[35],"they":[36],"can":[37,98,185],"be":[38,99],"easily":[39,100],"implemented.":[40],"There":[41],"are":[42,197],"two":[43],"classes":[44],"of":[45,54,60,67,79,88,106,156,173,189],"BSS.":[50,161],"first":[52],"class":[53,78],"algorithm":[55,80],"directly":[56],"accomplishes":[57],"matrix":[62],"pencil":[63],"which":[64,184],"is":[65,128,152,177,206],"composed":[66],"observed":[68],"signals":[69],"from":[70],"sensors":[71],"in":[72],"practical":[74],"system.":[75],"second":[77],"constructs":[81],"some":[82],"cost":[83],"function":[84],"with":[85],"form":[87],"Rayleigh":[90],"quotient":[91],"(GRQ),":[92],"and":[93,199],"its":[94],"corresponding":[95],"vector":[97,183],"obtained":[101],"by":[102],"GEVD.":[103,125],"However,":[104],"type":[108],"lack":[109],"reasonable":[110],"explanation.":[111],"focus":[116],"on":[117,135],"proposing":[118],"Besides,":[126],"it":[127],"pointed":[129],"out":[130],"that":[131,169],"their":[132],"separability":[133],"relies":[134],"signals\u2019":[137],"non-property:":[138],"non-Gaussianity":[139],"(statistical":[140],"non-property),":[141,144],"non-stationarity":[142],"(time":[143],"or":[145],"non-whiteness":[146],"(frequency":[147],"non-property).":[148],"Hence,":[149],"non-property":[151],"essential":[154],"characteristic":[155],"all":[157],"GEVD-based":[158],"Under":[162],"proposed":[164],"framework,":[166],"prove":[168],"any":[170],"eigenvector":[172],"same":[179],"as":[180],"de-mixing":[182],"successfully":[186],"separate":[187],"one":[188],"original":[191],"signals.":[193],"Finally,":[194],"simulation":[195],"experiments":[196],"made":[198],"theoretical":[200],"explanation":[201],"our":[203],"given.":[207]},"counts_by_year":[],"updated_date":"2026-06-06T09:05:17.133730","created_date":"2025-10-10T00:00:00"}