{"id":"https://openalex.org/W3015213667","doi":"https://doi.org/10.1109/icassp40776.2020.9053234","title":"Phase Reconstruction Based On Recurrent Phase Unwrapping With Deep Neural Networks","display_name":"Phase Reconstruction Based On Recurrent Phase Unwrapping With Deep Neural Networks","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3015213667","doi":"https://doi.org/10.1109/icassp40776.2020.9053234","mag":"3015213667"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9053234","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053234","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5042385500","display_name":"Yoshiki Masuyama","orcid":"https://orcid.org/0000-0002-5881-0474"},"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":"Yoshiki Masuyama","raw_affiliation_strings":["Waseda University,Department of Intermedia Art and Science,Tokyo,Japan","Department of Intermedia Art and Science, Waseda University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University,Department of Intermedia Art and Science,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]},{"raw_affiliation_string":"Department of Intermedia Art and Science, Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034837951","display_name":"Kohei Yatabe","orcid":"https://orcid.org/0000-0002-1345-0663"},"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":"Kohei Yatabe","raw_affiliation_strings":["Waseda University,Department of Intermedia Art and Science,Tokyo,Japan","Department of Intermedia Art and Science, Waseda University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University,Department of Intermedia Art and Science,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]},{"raw_affiliation_string":"Department of Intermedia Art and Science, Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"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 Media Intelligence Laboratories,Tokyo,Japan","NTT Media Intelligence Laboratories, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Media Intelligence Laboratories,Tokyo,Japan","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"NTT Media Intelligence Laboratories, Tokyo, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028428935","display_name":"Yasuhiro Oikawa","orcid":"https://orcid.org/0000-0002-9078-1714"},"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":"Yasuhiro Oikawa","raw_affiliation_strings":["Waseda University,Department of Intermedia Art and Science,Tokyo,Japan","Department of Intermedia Art and Science, Waseda University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University,Department of Intermedia Art and Science,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]},{"raw_affiliation_string":"Department of Intermedia Art and Science, Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054467679","display_name":"Noboru Harada","orcid":"https://orcid.org/0000-0002-1759-4533"},"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":"Noboru Harada","raw_affiliation_strings":["NTT Media Intelligence Laboratories,Tokyo,Japan","NTT Media Intelligence Laboratories, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Media Intelligence Laboratories,Tokyo,Japan","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"NTT Media Intelligence Laboratories, Tokyo, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.9577,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.92838836,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"826","last_page":"830"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","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/T10860","display_name":"Speech and Audio Processing","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/T11309","display_name":"Music and Audio Processing","score":0.9983000159263611,"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":0.996999979019165,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7204644680023193},{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.7060987949371338},{"id":"https://openalex.org/keywords/phase","display_name":"Phase (matter)","score":0.6975207328796387},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6081336736679077},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.5977498292922974},{"id":"https://openalex.org/keywords/signal-reconstruction","display_name":"Signal reconstruction","score":0.5571728944778442},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5380958914756775},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4702538251876831},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.4641832709312439},{"id":"https://openalex.org/keywords/amplitude","display_name":"Amplitude","score":0.4422226846218109},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.41084009408950806},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.404575914144516},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3661053776741028},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.34056615829467773},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.14393296837806702},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11051374673843384},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07486805319786072}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7204644680023193},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.7060987949371338},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.6975207328796387},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6081336736679077},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.5977498292922974},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.5571728944778442},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5380958914756775},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4702538251876831},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.4641832709312439},{"id":"https://openalex.org/C180205008","wikidata":"https://www.wikidata.org/wiki/Q159190","display_name":"Amplitude","level":2,"score":0.4422226846218109},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.41084009408950806},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.404575914144516},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3661053776741028},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.34056615829467773},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.14393296837806702},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11051374673843384},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07486805319786072},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"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/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp40776.2020.9053234","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053234","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W585975565","https://openalex.org/W1548018871","https://openalex.org/W2041498927","https://openalex.org/W2095072097","https://openalex.org/W2099520264","https://openalex.org/W2120847449","https://openalex.org/W2141998673","https://openalex.org/W2152859600","https://openalex.org/W2236262108","https://openalex.org/W2345844407","https://openalex.org/W2401387233","https://openalex.org/W2516594951","https://openalex.org/W2519091744","https://openalex.org/W2609317876","https://openalex.org/W2749881488","https://openalex.org/W2765486990","https://openalex.org/W2807065297","https://openalex.org/W2874689226","https://openalex.org/W2889134433","https://openalex.org/W2894785362","https://openalex.org/W2902806197","https://openalex.org/W2910577860","https://openalex.org/W2922391058","https://openalex.org/W2942514267","https://openalex.org/W2948211236","https://openalex.org/W2949382160","https://openalex.org/W2962905190","https://openalex.org/W2962911378","https://openalex.org/W2962956936","https://openalex.org/W2962981281","https://openalex.org/W2963237661","https://openalex.org/W2963609956","https://openalex.org/W2964171275","https://openalex.org/W2964328256","https://openalex.org/W3103301587","https://openalex.org/W4288594364","https://openalex.org/W6745289305","https://openalex.org/W6750842128","https://openalex.org/W6758675244","https://openalex.org/W6758708508","https://openalex.org/W6763100362"],"related_works":["https://openalex.org/W2530685530","https://openalex.org/W2011227383","https://openalex.org/W4375868962","https://openalex.org/W2088854863","https://openalex.org/W1976719989","https://openalex.org/W2942893872","https://openalex.org/W2065606036","https://openalex.org/W2367940508","https://openalex.org/W2908649630","https://openalex.org/W2964328256"],"abstract_inverted_index":{"Phase":[0],"reconstruction,":[1],"which":[2,91,109],"estimates":[3],"phase":[4,40,50,58,76,85,89,100,113,127],"from":[5,31],"a":[6,47,65,73,130],"given":[7],"amplitude":[8],"spectrogram,":[9],"is":[10,52,59,101,110],"an":[11,54],"active":[12],"research":[13],"field":[14],"in":[15],"acoustical":[16],"signal":[17],"processing":[18],"with":[19],"many":[20],"applications":[21],"including":[22],"audio":[23],"synthesis.":[24],"To":[25,67],"take":[26],"advantage":[27],"of":[28,46,64,88],"rich":[29],"knowledge":[30],"data,":[32],"several":[33],"studies":[34],"presented":[35],"deep":[36],"neural":[37],"network":[38],"(DNN)\u2013based":[39],"reconstruction":[41,51,77],"methods.":[42],"However,":[43],"the":[44,62,80,96,106,121,125],"training":[45],"DNN":[48],"for":[49],"not":[53],"easy":[55],"task":[56],"because":[57],"sensitive":[60],"to":[61,94],"shift":[63],"waveform.":[66],"overcome":[68],"this":[69],"problem,":[70],"we":[71],"propose":[72],"DNN-based":[74],"two-stage":[75],"method.":[78],"In":[79],"proposed":[81,122],"method,":[82],"DNNs":[83],"estimate":[84],"derivatives":[86],"instead":[87],"itself,":[90],"allows":[92],"us":[93],"avoid":[95],"sensitivity":[97],"problem.":[98],"Then,":[99],"recursively":[102],"estimated":[103,107],"based":[104],"on":[105],"derivatives,":[108],"named":[111],"recurrent":[112],"unwrapping":[114],"(RPU).":[115],"The":[116],"experimental":[117],"results":[118],"confirm":[119],"that":[120],"method":[123],"outperformed":[124],"direct":[126],"estimation":[128],"by":[129],"DNN.":[131]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":5}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}