{"id":"https://openalex.org/W3161192272","doi":"https://doi.org/10.1109/ssp49050.2021.9513811","title":"A Hypothesis Testing Approach to Nonstationary Source Separation","display_name":"A Hypothesis Testing Approach to Nonstationary Source Separation","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3161192272","doi":"https://doi.org/10.1109/ssp49050.2021.9513811","mag":"3161192272"},"language":"en","primary_location":{"id":"doi:10.1109/ssp49050.2021.9513811","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp49050.2021.9513811","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Statistical Signal Processing Workshop (SSP)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2105.06958","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069641751","display_name":"Reza Sameni","orcid":"https://orcid.org/0000-0003-4913-6825"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Reza Sameni","raw_affiliation_strings":["Emory University School of Medicine,Department of Biomedical Informatics,GA,USA,30322"],"affiliations":[{"raw_affiliation_string":"Emory University School of Medicine,Department of Biomedical Informatics,GA,USA,30322","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055562421","display_name":"Christian Jutten","orcid":"https://orcid.org/0000-0002-4477-4847"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christian Jutten","raw_affiliation_strings":["Emory University School of Medicine,Department of Biomedical Informatics,GA,USA,30322"],"affiliations":[{"raw_affiliation_string":"Emory University School of Medicine,Department of Biomedical Informatics,GA,USA,30322","institution_ids":["https://openalex.org/I150468666"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5069641751"],"corresponding_institution_ids":["https://openalex.org/I150468666"],"apc_list":null,"apc_paid":null,"fwci":0.1539,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.4163479,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"166","last_page":"170"},"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9793000221252441,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/blind-signal-separation","display_name":"Blind signal separation","score":0.8817940950393677},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6582289338111877},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6354144811630249},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5375885963439941},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5141565799713135},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.501692533493042},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4516420364379883},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4477899372577667},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.4345109760761261},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.42759907245635986},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.426935613155365},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42488569021224976},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4214567244052887},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4186333417892456},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.4153679609298706},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2781686782836914},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2616552412509918}],"concepts":[{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.8817940950393677},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6582289338111877},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6354144811630249},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5375885963439941},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5141565799713135},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.501692533493042},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4516420364379883},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4477899372577667},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.4345109760761261},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.42759907245635986},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.426935613155365},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42488569021224976},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4214567244052887},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4186333417892456},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4153679609298706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2781686782836914},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2616552412509918},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ssp49050.2021.9513811","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp49050.2021.9513811","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Statistical Signal Processing Workshop (SSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2105.06958","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.06958","pdf_url":"https://arxiv.org/pdf/2105.06958","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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2105.06958","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.06958","pdf_url":"https://arxiv.org/pdf/2105.06958","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":[{"id":"https://openalex.org/F4320334678","display_name":"European Research Council","ror":"https://ror.org/0472cxd90"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W651705667","https://openalex.org/W1548802052","https://openalex.org/W1562327279","https://openalex.org/W1598517949","https://openalex.org/W1755563775","https://openalex.org/W1977067929","https://openalex.org/W1980559142","https://openalex.org/W1992534575","https://openalex.org/W1995506038","https://openalex.org/W2020671695","https://openalex.org/W2044535354","https://openalex.org/W2099509424","https://openalex.org/W2124195644","https://openalex.org/W2124757684","https://openalex.org/W2127986617","https://openalex.org/W2133900413","https://openalex.org/W2134926797","https://openalex.org/W2142280324","https://openalex.org/W2148534488","https://openalex.org/W2250124065","https://openalex.org/W2478177260","https://openalex.org/W2502764805","https://openalex.org/W2515903151","https://openalex.org/W2937569262","https://openalex.org/W2969875468","https://openalex.org/W3021544465","https://openalex.org/W3098446347","https://openalex.org/W4205778870","https://openalex.org/W4251959444","https://openalex.org/W6678627578","https://openalex.org/W6726292805"],"related_works":["https://openalex.org/W1975632186","https://openalex.org/W3027745756","https://openalex.org/W3205213561","https://openalex.org/W2531880140","https://openalex.org/W2383482627","https://openalex.org/W2126145365","https://openalex.org/W2036609560","https://openalex.org/W346861917","https://openalex.org/W3024018414","https://openalex.org/W2392054573"],"abstract_inverted_index":{"The":[0,98],"extraction":[1],"of":[2,33,40,47,58,95],"nonstationary":[3,96],"signals":[4],"from":[5],"blind":[6],"and":[7,74],"semi-blind":[8,92],"multivariate":[9],"observations":[10],"is":[11,83,101],"a":[12,44,75,88],"recurrent":[13],"problem.":[14],"Numerous":[15],"algorithms":[16],"have":[17],"been":[18,50],"developed":[19],"for":[20,69],"this":[21],"problem,":[22],"which":[23,85],"are":[24,72],"based":[25,79],"on":[26,80],"the":[27,56,59],"exact":[28],"or":[29,35],"approximate":[30],"joint":[31,53],"diagonalization":[32,54],"second":[34],"higher":[36],"order":[37],"cumulant":[38],"matrices/tensors":[39],"multichannel":[41],"data.":[42],"While":[43],"great":[45],"body":[46],"research":[48],"has":[49],"dedicated":[51],"to":[52,91,103],"algorithms,":[55],"selection":[57],"diagonalized":[60],"matrix/tensor":[61],"set":[62],"remains":[63],"highly":[64],"problem-specific.":[65],"Herein,":[66],"various":[67],"methods":[68],"nonstationarity":[70],"identification":[71],"reviewed":[73],"new":[76],"general":[77],"frame-work":[78],"hypothesis":[81],"testing":[82],"proposed,":[84],"results":[86],"in":[87],"classification/clustering":[89],"perspective":[90],"source":[93],"separation":[94],"components.":[97],"proposed":[99],"method":[100],"applied":[102],"noninvasive":[104],"fetal":[105],"ECG":[106],"extraction,":[107],"as":[108],"case":[109],"study.":[110]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}