{"id":"https://openalex.org/W2400027214","doi":"https://doi.org/10.1109/icassp.2016.7471712","title":"Pinpoint extraction of distant sound source based on DNN mapping from multiple beamforming outputs to prior SNR","display_name":"Pinpoint extraction of distant sound source based on DNN mapping from multiple beamforming outputs to prior SNR","publication_year":2016,"publication_date":"2016-03-01","ids":{"openalex":"https://openalex.org/W2400027214","doi":"https://doi.org/10.1109/icassp.2016.7471712","mag":"2400027214"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2016.7471712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7471712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5046170826","display_name":"Kenta Niwa","orcid":"https://orcid.org/0000-0002-6911-0238"},"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":"Kenta Niwa","raw_affiliation_strings":["NTT Corporation, NTT Media Intelligence Laboratories, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Corporation, NTT Media Intelligence Laboratories, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101503856","display_name":"Yuma Koizumi","orcid":"https://orcid.org/0000-0003-3645-6213"},"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":"Yuma Koizumi","raw_affiliation_strings":["NTT Corporation, NTT Media Intelligence Laboratories, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Corporation, NTT Media Intelligence Laboratories, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002983657","display_name":"Tomoko Kawase","orcid":"https://orcid.org/0000-0002-0670-3319"},"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":"Tomoko Kawase","raw_affiliation_strings":["NTT Corporation, NTT Media Intelligence Laboratories, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Corporation, NTT Media Intelligence Laboratories, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109046289","display_name":"Kazunori Kobayashi","orcid":null},"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":"Kazunori Kobayashi","raw_affiliation_strings":["NTT Corporation, NTT Media Intelligence Laboratories, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Corporation, NTT Media Intelligence Laboratories, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069841141","display_name":"Yusuke Hioka","orcid":"https://orcid.org/0000-0003-3380-9677"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Yusuke Hioka","raw_affiliation_strings":["Department of Mechanical Engineering, University of Auckland, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, University of Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.5736,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.93661683,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"15","issue":null,"first_page":"435","last_page":"439"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","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/T10860","display_name":"Speech and Audio Processing","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/T10822","display_name":"Acoustic Wave Phenomena Research","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9980000257492065,"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/beamforming","display_name":"Beamforming","score":0.8864634037017822},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7270587086677551},{"id":"https://openalex.org/keywords/microphone-array","display_name":"Microphone array","score":0.6893104910850525},{"id":"https://openalex.org/keywords/microphone","display_name":"Microphone","score":0.6039311289787292},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.5094380974769592},{"id":"https://openalex.org/keywords/wiener-filter","display_name":"Wiener filter","score":0.49674874544143677},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.48663291335105896},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.48207002878189087},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4485272467136383},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43724238872528076},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43037179112434387},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40236231684684753},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.3300083875656128},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.17648789286613464},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10444959998130798}],"concepts":[{"id":"https://openalex.org/C54197355","wikidata":"https://www.wikidata.org/wiki/Q5782992","display_name":"Beamforming","level":2,"score":0.8864634037017822},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7270587086677551},{"id":"https://openalex.org/C2778806681","wikidata":"https://www.wikidata.org/wiki/Q907293","display_name":"Microphone array","level":4,"score":0.6893104910850525},{"id":"https://openalex.org/C2778263558","wikidata":"https://www.wikidata.org/wiki/Q46384","display_name":"Microphone","level":3,"score":0.6039311289787292},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.5094380974769592},{"id":"https://openalex.org/C18537770","wikidata":"https://www.wikidata.org/wiki/Q25523","display_name":"Wiener filter","level":2,"score":0.49674874544143677},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48663291335105896},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.48207002878189087},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4485272467136383},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43724238872528076},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43037179112434387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40236231684684753},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.3300083875656128},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.17648789286613464},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10444959998130798},{"id":"https://openalex.org/C68115822","wikidata":"https://www.wikidata.org/wiki/Q1068172","display_name":"Sound pressure","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2016.7471712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7471712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W653761051","https://openalex.org/W1483320338","https://openalex.org/W1515932869","https://openalex.org/W1544785557","https://openalex.org/W1562552815","https://openalex.org/W1667950888","https://openalex.org/W1674726644","https://openalex.org/W1931312565","https://openalex.org/W1973746598","https://openalex.org/W1981617403","https://openalex.org/W1987213958","https://openalex.org/W2005981242","https://openalex.org/W2013020033","https://openalex.org/W2035976437","https://openalex.org/W2066460453","https://openalex.org/W2099655464","https://openalex.org/W2111344436","https://openalex.org/W2116064496","https://openalex.org/W2133475491","https://openalex.org/W2136922672","https://openalex.org/W2138908886","https://openalex.org/W2144244295","https://openalex.org/W2149693148","https://openalex.org/W2158995216","https://openalex.org/W2159158911","https://openalex.org/W2169975823","https://openalex.org/W2890883107","https://openalex.org/W3152352576","https://openalex.org/W3207342693","https://openalex.org/W4231109964","https://openalex.org/W6653477362","https://openalex.org/W6683587825","https://openalex.org/W6684992147","https://openalex.org/W7023653924"],"related_works":["https://openalex.org/W2896203542","https://openalex.org/W201299990","https://openalex.org/W876328409","https://openalex.org/W4319068615","https://openalex.org/W1879255185","https://openalex.org/W2148473678","https://openalex.org/W2393227939","https://openalex.org/W3004719695","https://openalex.org/W2120442551","https://openalex.org/W2769861442"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,71],"method":[3,67],"for":[4,14,19],"estimating":[5],"the":[6,16,34,44,53,63,75,95,102],"prior":[7,45],"signal-to-noise":[8],"ratio":[9],"(SNR),":[10],"which":[11,78],"is":[12],"used":[13],"calculating":[15],"Wiener":[17],"filter":[18],"distant":[20,88],"sound":[21,89],"source":[22,97],"extraction,":[23],"from":[24],"output":[25],"signals":[26],"of":[27,77],"beamforming":[28,51],"using":[29,62],"statistical":[30],"mapping":[31,61],"based":[32],"on":[33],"deep":[35],"neural":[36],"network":[37],"(DNN).":[38],"Since":[39],"informative":[40],"features":[41],"to":[42,70,81,86],"estimate":[43],"SNR":[46,54],"are":[47],"included":[48],"in":[49],"multiple":[50],"outputs,":[52],"can":[55],"be":[56],"accurately":[57],"estimated":[58],"by":[59],"this":[60],"DNN.":[64],"The":[65],"proposed":[66,103],"was":[68,79,98],"applied":[69],"large":[72],"microphone":[73],"array,":[74],"design":[76],"optimized":[80],"form":[82],"effective":[83],"directivity":[84],"patterns":[85],"extract":[87],"sources.":[90],"Experimental":[91],"results":[92],"proved":[93],"that":[94],"target":[96],"clearly":[99],"extracted":[100],"with":[101],"method.":[104]},"counts_by_year":[{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}