{"id":"https://openalex.org/W3116386850","doi":"https://doi.org/10.23919/eusipco47968.2020.9287783","title":"A Riemannian approach to blind separation of t-distributed sources","display_name":"A Riemannian approach to blind separation of t-distributed sources","publication_year":2020,"publication_date":"2020-12-18","ids":{"openalex":"https://openalex.org/W3116386850","doi":"https://doi.org/10.23919/eusipco47968.2020.9287783","mag":"3116386850"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco47968.2020.9287783","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco47968.2020.9287783","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th 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://hal.univ-grenoble-alpes.fr/hal-02988356","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079968302","display_name":"Florent Bouchard","orcid":"https://orcid.org/0000-0003-3003-7317"},"institutions":[{"id":"https://openalex.org/I70900168","display_name":"Universit\u00e9 Savoie Mont Blanc","ror":"https://ror.org/04gqg1a07","country_code":"FR","type":"education","lineage":["https://openalex.org/I70900168"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Florent Bouchard","raw_affiliation_strings":["LISTIC, Univ. Savoie Mont Blanc, France","LISTIC - Laboratoire d'Informatique, Syst\u00e8mes, Traitement de l'Information et de la Connaissance (BP 80439 74944 ANNECY LE VIEUX Cedex - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LISTIC, Univ. Savoie Mont Blanc, France","institution_ids":["https://openalex.org/I70900168"]},{"raw_affiliation_string":"LISTIC - Laboratoire d'Informatique, Syst\u00e8mes, Traitement de l'Information et de la Connaissance (BP 80439 74944 ANNECY LE VIEUX Cedex - France)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059566872","display_name":"Arnaud Breloy","orcid":"https://orcid.org/0000-0002-3802-9015"},"institutions":[{"id":"https://openalex.org/I40434647","display_name":"Universit\u00e9 Paris Nanterre","ror":"https://ror.org/013bkhk48","country_code":"FR","type":"education","lineage":["https://openalex.org/I40434647"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Arnaud Breloy","raw_affiliation_strings":["LEME, Univ. Paris Nanterre, France","LEME - Laboratoire Energ\u00e9tique M\u00e9canique Electromagn\u00e9tisme (LEME EA4416\r\nUniversit\u00e9 Paris Nanterre\r\n50 rue de S\u00e8vres\r\n92410 Ville-d'Avray - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LEME, Univ. Paris Nanterre, France","institution_ids":["https://openalex.org/I40434647"]},{"raw_affiliation_string":"LEME - Laboratoire Energ\u00e9tique M\u00e9canique Electromagn\u00e9tisme (LEME EA4416\r\nUniversit\u00e9 Paris Nanterre\r\n50 rue de S\u00e8vres\r\n92410 Ville-d'Avray - France)","institution_ids":["https://openalex.org/I40434647"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057632387","display_name":"Guillaume Ginolhac","orcid":"https://orcid.org/0000-0001-9318-028X"},"institutions":[{"id":"https://openalex.org/I70900168","display_name":"Universit\u00e9 Savoie Mont Blanc","ror":"https://ror.org/04gqg1a07","country_code":"FR","type":"education","lineage":["https://openalex.org/I70900168"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Guillaume Ginolhac","raw_affiliation_strings":["LISTIC, Univ. Savoie Mont Blanc, France","LISTIC - Laboratoire d'Informatique, Syst\u00e8mes, Traitement de l'Information et de la Connaissance (BP 80439 74944 ANNECY LE VIEUX Cedex - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LISTIC, Univ. Savoie Mont Blanc, France","institution_ids":["https://openalex.org/I70900168"]},{"raw_affiliation_string":"LISTIC - Laboratoire d'Informatique, Syst\u00e8mes, Traitement de l'Information et de la Connaissance (BP 80439 74944 ANNECY LE VIEUX Cedex - France)","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044736586","display_name":"Alexandre Renaux","orcid":"https://orcid.org/0000-0003-3413-1765"},"institutions":[{"id":"https://openalex.org/I102197404","display_name":"Universit\u00e9 Paris-Sud","ror":"https://ror.org/028rypz17","country_code":"FR","type":"education","lineage":["https://openalex.org/I102197404"]},{"id":"https://openalex.org/I4210097418","display_name":"Laboratoire des signaux et syst\u00e8mes","ror":"https://ror.org/00skw9v43","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I277688954","https://openalex.org/I277688954","https://openalex.org/I4210097418","https://openalex.org/I4210107720"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Alexandre Renaux","raw_affiliation_strings":["L2S, Univ. Paris Sud, France","L2S - Laboratoire des signaux et syst\u00e8mes (Plateau de Moulon 3 rue Joliot Curie 91192 GIF SUR YVETTE CEDEX - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"L2S, Univ. Paris Sud, France","institution_ids":["https://openalex.org/I102197404"]},{"raw_affiliation_string":"L2S - Laboratoire des signaux et syst\u00e8mes (Plateau de Moulon 3 rue Joliot Curie 91192 GIF SUR YVETTE CEDEX - France)","institution_ids":["https://openalex.org/I4210097418"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"965","last_page":"969"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","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/T11447","display_name":"Blind Source Separation Techniques","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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9639999866485596,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/manifold","display_name":"Manifold (fluid mechanics)","score":0.7309356927871704},{"id":"https://openalex.org/keywords/generalized-normal-distribution","display_name":"Generalized normal distribution","score":0.6237847208976746},{"id":"https://openalex.org/keywords/blind-signal-separation","display_name":"Blind signal separation","score":0.5798051953315735},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5702752470970154},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.5464762449264526},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.5156065821647644},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.5101433396339417},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49718931317329407},{"id":"https://openalex.org/keywords/separation","display_name":"Separation (statistics)","score":0.49457523226737976},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.49443483352661133},{"id":"https://openalex.org/keywords/source-separation","display_name":"Source separation","score":0.4847245216369629},{"id":"https://openalex.org/keywords/riemannian-manifold","display_name":"Riemannian manifold","score":0.4537912905216217},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4256824254989624},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4046822786331177},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4038708209991455},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36124417185783386},{"id":"https://openalex.org/keywords/normal-distribution","display_name":"Normal distribution","score":0.18543756008148193},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16359999775886536},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.15068098902702332},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.13731515407562256},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07149815559387207}],"concepts":[{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.7309356927871704},{"id":"https://openalex.org/C171383496","wikidata":"https://www.wikidata.org/wiki/Q2497477","display_name":"Generalized normal distribution","level":3,"score":0.6237847208976746},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.5798051953315735},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5702752470970154},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.5464762449264526},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.5156065821647644},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.5101433396339417},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49718931317329407},{"id":"https://openalex.org/C2776061190","wikidata":"https://www.wikidata.org/wiki/Q7451805","display_name":"Separation (statistics)","level":2,"score":0.49457523226737976},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.49443483352661133},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.4847245216369629},{"id":"https://openalex.org/C2779593128","wikidata":"https://www.wikidata.org/wiki/Q632814","display_name":"Riemannian manifold","level":2,"score":0.4537912905216217},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4256824254989624},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4046822786331177},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4038708209991455},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36124417185783386},{"id":"https://openalex.org/C102094743","wikidata":"https://www.wikidata.org/wiki/Q133871","display_name":"Normal distribution","level":2,"score":0.18543756008148193},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16359999775886536},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.15068098902702332},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.13731515407562256},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07149815559387207},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/eusipco47968.2020.9287783","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco47968.2020.9287783","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-02988356v1","is_oa":true,"landing_page_url":"https://hal.univ-grenoble-alpes.fr/hal-02988356","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"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":"2020 28th European Signal Processing Conference (EUSIPCO), Jan 2021, Amsterdam, Netherlands. &#x27E8;10.23919/eusipco47968.2020.9287783&#x27E9;","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-02988356v1","is_oa":true,"landing_page_url":"https://hal.univ-grenoble-alpes.fr/hal-02988356","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"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":"2020 28th European Signal Processing Conference (EUSIPCO), Jan 2021, Amsterdam, Netherlands. &#x27E8;10.23919/eusipco47968.2020.9287783&#x27E9;","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5027824404","display_name":"Modern Adaptive Radar: Great Advances in Robust and Inference Techniques and Application","funder_award_id":"ANR-17-ASTR-0015","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"}],"funders":[{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W585133165","https://openalex.org/W1755563775","https://openalex.org/W1804110266","https://openalex.org/W1832327366","https://openalex.org/W1992511687","https://openalex.org/W1996355918","https://openalex.org/W2019543362","https://openalex.org/W2098760944","https://openalex.org/W2099741732","https://openalex.org/W2124195644","https://openalex.org/W2124757684","https://openalex.org/W2142638745","https://openalex.org/W2152502807","https://openalex.org/W2155779374","https://openalex.org/W2160559331","https://openalex.org/W2167623372","https://openalex.org/W2792697231","https://openalex.org/W2963684104","https://openalex.org/W3000334627","https://openalex.org/W3015826334","https://openalex.org/W4205293427","https://openalex.org/W6684641359","https://openalex.org/W6725705423"],"related_works":["https://openalex.org/W1509813908","https://openalex.org/W2031820693","https://openalex.org/W1910172735","https://openalex.org/W2107364365","https://openalex.org/W2118307209","https://openalex.org/W2113403277","https://openalex.org/W2137288760","https://openalex.org/W2889447638","https://openalex.org/W2539388437","https://openalex.org/W1785857632"],"abstract_inverted_index":{"The":[0,108],"blind":[1,57],"source":[2,58],"separation":[3,59],"problem":[4],"is":[5,69,83,114],"considered":[6,67],"through":[7],"the":[8,18,40,46,49,63,66,73,80,88,111],"approach":[9],"based":[10,61,95],"on":[11,62,79,116],"non-stationarity":[12],"and":[13],"coloration.":[14],"In":[15,26],"both":[16],"cases,":[17],"sources":[19,37],"are":[20,103],"usually":[21],"assumed":[22],"to":[23,35,105],"be":[24],"Gaussian.":[25],"this":[27,53,100,106],"paper,":[28],"we":[29],"extend":[30],"previous":[31],"works":[32],"in":[33,52],"order":[34,94],"handle":[36],"drawn":[38],"from":[39],"multivariate":[41],"Student":[42],"t-distribution.":[43],"After":[44],"studying":[45],"structure":[47],"of":[48,65,87,110],"parameter":[50,81,101],"manifold":[51,82,102],"case,":[54],"a":[55],"new":[56],"criterion":[60],"log-likelihood":[64],"distribution":[68],"proposed.":[70],"To":[71],"solve":[72],"resulting":[74],"optimization":[75,78,97],"problem,":[76],"Riemannian":[77],"leveraged.":[84],"Practical":[85],"expressions":[86],"mathematical":[89],"tools":[90],"required":[91],"by":[92],"first":[93],"Riemmanian":[96],"methods":[98],"for":[99],"derived":[104],"end.":[107],"performance":[109],"proposed":[112],"method":[113],"illustrated":[115],"simulated":[117],"data.":[118]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
