{"id":"https://openalex.org/W2151628769","doi":"https://doi.org/10.1109/tasl.2008.2011527","title":"Frequency-Domain Pearson Distribution Approach for Independent Component Analysis (FD-Pearson-ICA) in Blind Source Separation","display_name":"Frequency-Domain Pearson Distribution Approach for Independent Component Analysis (FD-Pearson-ICA) in Blind Source Separation","publication_year":2009,"publication_date":"2009-03-24","ids":{"openalex":"https://openalex.org/W2151628769","doi":"https://doi.org/10.1109/tasl.2008.2011527","mag":"2151628769"},"language":"en","primary_location":{"id":"doi:10.1109/tasl.2008.2011527","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tasl.2008.2011527","pdf_url":null,"source":{"id":"https://openalex.org/S199497470","display_name":"IEEE Transactions on Audio Speech and Language Processing","issn_l":"1558-7916","issn":["1558-7916","1558-7924"],"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 Transactions on Audio, Speech, and Language Processing","raw_type":"journal-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/A5041882786","display_name":"Hiroko Kato Solvang","orcid":"https://orcid.org/0000-0002-0330-4670"},"institutions":[{"id":"https://openalex.org/I1281400175","display_name":"Oslo University Hospital","ror":"https://ror.org/00j9c2840","country_code":"NO","type":"healthcare","lineage":["https://openalex.org/I1281400175"]},{"id":"https://openalex.org/I184942183","display_name":"University of Oslo","ror":"https://ror.org/01xtthb56","country_code":"NO","type":"education","lineage":["https://openalex.org/I184942183"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Hiroko Kato Solvang","raw_affiliation_strings":["Department of Biostatistics, Institute of Basic Medical Science, University of Oslo, Oslo, Norway","Norwegian Radium Hospital, Rikshospitalet University Hospital, Oslo, Norway"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Institute of Basic Medical Science, University of Oslo, Oslo, Norway","institution_ids":["https://openalex.org/I184942183"]},{"raw_affiliation_string":"Norwegian Radium Hospital, Rikshospitalet University Hospital, Oslo, Norway","institution_ids":["https://openalex.org/I1281400175"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025119626","display_name":"Yuichi Nagahara","orcid":null},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuichi Nagahara","raw_affiliation_strings":["Meiji University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meiji University, Tokyo, Japan","institution_ids":["https://openalex.org/I16656306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009309584","display_name":"Shoko Araki","orcid":"https://orcid.org/0000-0003-4363-4305"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shoko Araki","raw_affiliation_strings":["NTT Communication Science Laboratories, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007032557","display_name":"Hiroshi Sawada","orcid":"https://orcid.org/0000-0002-4831-9286"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroshi Sawada","raw_affiliation_strings":["NTT Communication Science Laboratories, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075702573","display_name":"Shoji Makino","orcid":"https://orcid.org/0000-0003-1934-640X"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shoji Makino","raw_affiliation_strings":["NTT Communication Science Laboratories, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.5185,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.88828495,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"17","issue":"4","first_page":"639","last_page":"649"},"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":0.9954000115394592,"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":0.9553999900817871,"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/independent-component-analysis","display_name":"Independent component analysis","score":0.7343936562538147},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.6630566120147705},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5998985767364502},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.5559372305870056},{"id":"https://openalex.org/keywords/generalized-normal-distribution","display_name":"Generalized normal distribution","score":0.5180764198303223},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.443194180727005},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38881200551986694},{"id":"https://openalex.org/keywords/normal-distribution","display_name":"Normal distribution","score":0.36295995116233826},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.34302252531051636},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.33463743329048157},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32704758644104004},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2800445556640625},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.26098185777664185},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.10726511478424072}],"concepts":[{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.7343936562538147},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.6630566120147705},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5998985767364502},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.5559372305870056},{"id":"https://openalex.org/C171383496","wikidata":"https://www.wikidata.org/wiki/Q2497477","display_name":"Generalized normal distribution","level":3,"score":0.5180764198303223},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.443194180727005},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38881200551986694},{"id":"https://openalex.org/C102094743","wikidata":"https://www.wikidata.org/wiki/Q133871","display_name":"Normal distribution","level":2,"score":0.36295995116233826},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.34302252531051636},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.33463743329048157},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32704758644104004},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2800445556640625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.26098185777664185},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.10726511478424072}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tasl.2008.2011527","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tasl.2008.2011527","pdf_url":null,"source":{"id":"https://openalex.org/S199497470","display_name":"IEEE Transactions on Audio Speech and Language Processing","issn_l":"1558-7916","issn":["1558-7916","1558-7924"],"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 Transactions on Audio, Speech, and Language Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W190532438","https://openalex.org/W1491770470","https://openalex.org/W1510095622","https://openalex.org/W1523943344","https://openalex.org/W1531450456","https://openalex.org/W1548802052","https://openalex.org/W1974388905","https://openalex.org/W1979593580","https://openalex.org/W1979850790","https://openalex.org/W1991489359","https://openalex.org/W2027884847","https://openalex.org/W2042353857","https://openalex.org/W2044072034","https://openalex.org/W2052086329","https://openalex.org/W2073832517","https://openalex.org/W2096855653","https://openalex.org/W2109869028","https://openalex.org/W2116766578","https://openalex.org/W2120107643","https://openalex.org/W2121788372","https://openalex.org/W2128911505","https://openalex.org/W2128967371","https://openalex.org/W2141224535","https://openalex.org/W2147614211","https://openalex.org/W2149709356","https://openalex.org/W2540419083","https://openalex.org/W4205778870","https://openalex.org/W4242259102","https://openalex.org/W4310638450","https://openalex.org/W6631462732"],"related_works":["https://openalex.org/W2072769933","https://openalex.org/W1981359679","https://openalex.org/W4313344354","https://openalex.org/W4283169561","https://openalex.org/W2130221871","https://openalex.org/W2419056419","https://openalex.org/W3106777427","https://openalex.org/W1594432845","https://openalex.org/W2086094787","https://openalex.org/W2912733426"],"abstract_inverted_index":{"In":[0],"frequency-domain":[1,26,168,177],"blind":[2],"source":[3,27,178],"separation":[4,50,114,147,248],"(BSS)":[5],"for":[6,73,132,140],"speech":[7,81,151],"with":[8,56,91,149,154,195,260,288],"independent":[9],"component":[10],"analysis":[11],"(ICA),":[12],"a":[13,33,79,104,214],"practical":[14],"parametric":[15,105,224],"Pearson":[16,106,129,141],"distribution":[17,24,64,98,107,130,142,253],"system":[18],"is":[19,124],"used":[20],"to":[21,69,111,173,271],"model":[22],"the":[23,45,70,96,113,117,127,166,174,182,185,201,210,218,229,236,239,243,261,275,283],"of":[25,176,206,231,238],"signals.":[28,179],"ICA":[29,118],"adaptation":[30],"rules":[31],"have":[32],"score":[34,71,122,220,225],"function":[35,72,123,221,226],"determined":[36],"by":[37,102,126,190,286],"an":[38],"approximated":[39],"signal":[40,82,97],"distribution.":[41],"Approximation":[42],"based":[43,212],"on":[44,213],"data":[46],"may":[47],"produce":[48],"better":[49],"performance":[51,211,277],"than":[52],"we":[53,93,234],"can":[54],"obtain":[55],"ICA.":[57],"Previously,":[58],"conventional":[59,196,289],"hyperbolic":[60],"tangent$(tanh)$or":[61],"generalized":[62],"Gaussian":[63],"(GGD)":[65],"was":[66,269],"uniformly":[67],"applied":[68],"all":[74],"frequency":[75,101,134,259],"bins,":[76],"even":[77],"though":[78],"wideband":[80],"has":[83],"different":[84,87],"distributions":[85],"at":[86,99,257,265],"frequencies.":[88],"To":[89],"deal":[90],"this,":[92],"propose":[94],"modeling":[95],"each":[100,133],"adopting":[103],"and":[108,145,222],"employing":[109],"it":[110,268],"optimize":[112],"matrix":[115],"in":[116,282],"learning":[119],"process.":[120],"The":[121],"estimated":[125,223,256],"appropriate":[128,262],"parameters":[131,254,263],"bin.":[135],"We":[136],"devised":[137],"three":[138],"methods":[139],"parameter":[143],"estimation":[144],"conducted":[146],"experiments":[148],"real":[150],"signals":[152],"convolved":[153],"actual":[155],"room":[156],"impulse":[157],"responses$(T_{60}=130\\":[158],"{\\hbox":[159],"{ms}})$.":[160],"Our":[161],"experimental":[162],"results":[163],"show":[164],"that":[165],"proposed":[167,240,244],"Pearson-ICA":[169],"(FD-Pearson-ICA)":[170],"adapted":[171],"well":[172],"characteristics":[175],"By":[180,250],"applying":[181],"FD-Pearson-ICA":[183,207,276],"performance,":[184],"signal-to-interference":[186,202],"ratio":[187,203],"significantly":[188],"improved":[189],"around":[191],"2\u20133":[192],"dB":[193],"compared":[194],"nonlinear":[197,290],"functions.":[198,291],"Even":[199],"if":[200],"(SIR)":[204],"values":[205],"were":[208],"poor,":[209],"disparity":[215],"measure":[216],"between":[217],"true":[219],"clearly":[227],"showed":[228],"advantage":[230],"FD-Pearson-ICA.":[232],"Furthermore,":[233],"confirmed":[235],"optimum":[237],"approach":[241,245],"for/optimized":[242],"as":[246],"regards":[247],"performance.":[249],"combining":[251],"individual":[252],"directly":[255],"low":[258],"optimized":[264],"high":[266],"frequency,":[267],"possible":[270],"both":[272],"reasonably":[273],"improve":[274],"without":[278],"any":[279],"significant":[280],"increase":[281],"computational":[284],"burden":[285],"comparison":[287]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":3}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}