{"id":"https://openalex.org/W4403126688","doi":"https://doi.org/10.1109/iwaenc61483.2024.10694473","title":"Accurate Delayed Source Model for Multi-Frame Full-Rank Spatial Covariance Analysis","display_name":"Accurate Delayed Source Model for Multi-Frame Full-Rank Spatial Covariance Analysis","publication_year":2024,"publication_date":"2024-09-09","ids":{"openalex":"https://openalex.org/W4403126688","doi":"https://doi.org/10.1109/iwaenc61483.2024.10694473"},"language":"en","primary_location":{"id":"doi:10.1109/iwaenc61483.2024.10694473","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwaenc61483.2024.10694473","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 18th International Workshop on Acoustic Signal Enhancement (IWAENC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5055750702","display_name":"Shinya Furunaga","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shinya Furunaga","raw_affiliation_strings":["Waseda University,Fukuoka,Japan,808-0135"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University,Fukuoka,Japan,808-0135","institution_ids":["https://openalex.org/I150744194"]}]},{"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 Corporation,Kyoto,Japan,619-0237"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Corporation,Kyoto,Japan,619-0237","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084590882","display_name":"Rintaro Ikeshita","orcid":"https://orcid.org/0000-0003-2608-1999"},"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":"Rintaro Ikeshita","raw_affiliation_strings":["NTT Corporation,Kyoto,Japan,619-0237"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Corporation,Kyoto,Japan,619-0237","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021240106","display_name":"Tomohiro Nakatani","orcid":"https://orcid.org/0000-0002-7487-7150"},"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":"Tomohiro Nakatani","raw_affiliation_strings":["NTT Corporation,Kyoto,Japan,619-0237"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Corporation,Kyoto,Japan,619-0237","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/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shoji Makino","raw_affiliation_strings":["Waseda University,Fukuoka,Japan,808-0135"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University,Fukuoka,Japan,808-0135","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"170","last_page":"174"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.8580999970436096,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.8580999970436096,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10770","display_name":"Soil Geostatistics and Mapping","score":0.82669997215271,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.7440133094787598},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6159844994544983},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.563494086265564},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5207086205482483},{"id":"https://openalex.org/keywords/analysis-of-covariance","display_name":"Analysis of covariance","score":0.468395471572876},{"id":"https://openalex.org/keywords/covariance-function","display_name":"Covariance function","score":0.43213266134262085},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4280223250389099},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3438267707824707},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3218822479248047},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24958118796348572},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.14993754029273987},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1043754518032074},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.08222329616546631}],"concepts":[{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.7440133094787598},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6159844994544983},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.563494086265564},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5207086205482483},{"id":"https://openalex.org/C119340705","wikidata":"https://www.wikidata.org/wiki/Q1628597","display_name":"Analysis of covariance","level":2,"score":0.468395471572876},{"id":"https://openalex.org/C137250428","wikidata":"https://www.wikidata.org/wiki/Q5178897","display_name":"Covariance function","level":3,"score":0.43213266134262085},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4280223250389099},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3438267707824707},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3218822479248047},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24958118796348572},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.14993754029273987},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1043754518032074},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.08222329616546631}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwaenc61483.2024.10694473","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwaenc61483.2024.10694473","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 18th International Workshop on Acoustic Signal Enhancement (IWAENC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W184454132","https://openalex.org/W285277413","https://openalex.org/W1505878979","https://openalex.org/W1981755271","https://openalex.org/W1996355918","https://openalex.org/W2002146237","https://openalex.org/W2093258380","https://openalex.org/W2097584344","https://openalex.org/W2099741732","https://openalex.org/W2108384452","https://openalex.org/W2123649031","https://openalex.org/W2127851351","https://openalex.org/W2143027228","https://openalex.org/W2164502538","https://openalex.org/W2168273590","https://openalex.org/W2903219940","https://openalex.org/W2918296821","https://openalex.org/W2964080410","https://openalex.org/W3016114588","https://openalex.org/W3151596526","https://openalex.org/W3161538827","https://openalex.org/W3163542373","https://openalex.org/W4225326943","https://openalex.org/W4386536172","https://openalex.org/W6677083428","https://openalex.org/W6805885086"],"related_works":["https://openalex.org/W4235479087","https://openalex.org/W2386803684","https://openalex.org/W1971337326","https://openalex.org/W3178402891","https://openalex.org/W651058035","https://openalex.org/W143964308","https://openalex.org/W2073389045","https://openalex.org/W2046078371","https://openalex.org/W4315750325","https://openalex.org/W4297832768"],"abstract_inverted_index":{"Multi-frame":[0],"Full-rank":[1],"Spatial":[2],"Covariance":[3],"Analysis":[4],"(mfFCA)":[5],"is":[6,33],"a":[7,10,54],"technique":[8],"for":[9,84,101],"blind":[11],"source":[12,45,66],"separation":[13],"method":[14,94],"and":[15,29,43,64,80],"can":[16],"be":[17],"applied":[18],"to":[19,104],"reverberant":[20],"underdetermined":[21],"conditions":[22],"where":[23],"the":[24,27,30,62,85,92,98,102],"sources":[25,107],"outnumber":[26],"microphones":[28],"reverberation":[31],"time":[32],"long.":[34],"This":[35,51],"model,":[36],"however,":[37],"does":[38],"not":[39],"express":[40],"all":[41],"direct":[42,63],"delayed":[44,65],"components":[46],"in":[47],"multi-frame":[48],"observation":[49],"vector.":[50],"paper":[52],"proposes":[53],"new":[55,78],"model":[56],"that":[57,91],"takes":[58],"into":[59],"account":[60],"accurately":[61],"components,":[67],"by":[68],"introducing":[69],"delay-wise":[70],"spatial":[71],"covariance":[72],"matrices.":[73],"We":[74],"have":[75],"then":[76],"derived":[77],"expectation-maximization":[79],"multiplicative":[81],"update":[82],"algorithms":[83],"proposed":[86,93],"model.":[87],"Experimental":[88],"results":[89],"show":[90],"performed":[95],"better":[96],"than":[97],"conventional":[99],"mfFCA":[100],"task":[103],"separate":[105],"three":[106],"with":[108],"two":[109],"microphones.":[110]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}