{"id":"https://openalex.org/W4402571719","doi":"https://doi.org/10.1109/newcas58973.2024.10666311","title":"Sonar Dereverberation via Independent Component Analysis and Deep Learning","display_name":"Sonar Dereverberation via Independent Component Analysis and Deep Learning","publication_year":2024,"publication_date":"2024-06-16","ids":{"openalex":"https://openalex.org/W4402571719","doi":"https://doi.org/10.1109/newcas58973.2024.10666311"},"language":"en","primary_location":{"id":"doi:10.1109/newcas58973.2024.10666311","is_oa":false,"landing_page_url":"https://doi.org/10.1109/newcas58973.2024.10666311","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 22nd IEEE Interregional NEWCAS Conference (NEWCAS)","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/A5100451331","display_name":"Xuyang Wang","orcid":"https://orcid.org/0000-0001-5718-0231"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuyang Wang","raw_affiliation_strings":["School of Integrated Circuits, Tsinghua University,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Integrated Circuits, Tsinghua University,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102699216","display_name":"Kaihui Zeng","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaihui Zeng","raw_affiliation_strings":["School of Integrated Circuits, Tsinghua University,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Integrated Circuits, Tsinghua University,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101764542","display_name":"Guolin Li","orcid":"https://orcid.org/0000-0001-9934-4230"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guolin Li","raw_affiliation_strings":["Tsinghua University,Department of Electronic Engineering,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Electronic Engineering,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100642396","display_name":"Xiang Xie","orcid":"https://orcid.org/0000-0002-6895-3145"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Xie","raw_affiliation_strings":["School of Integrated Circuits, Tsinghua University,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Integrated Circuits, Tsinghua University,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017756538","display_name":"Zhihua Wang","orcid":"https://orcid.org/0000-0001-8976-3212"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihua Wang","raw_affiliation_strings":["School of Integrated Circuits, Tsinghua University,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Integrated Circuits, Tsinghua University,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.2142,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.49956786,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12549","display_name":"Image and Object Detection Techniques","score":0.8565000295639038,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12549","display_name":"Image and Object Detection Techniques","score":0.8565000295639038,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.8428000211715698,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T13114","display_name":"Image Processing Techniques and Applications","score":0.7562999725341797,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/sonar","display_name":"Sonar","score":0.8285725116729736},{"id":"https://openalex.org/keywords/independent-component-analysis","display_name":"Independent component analysis","score":0.755916178226471},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.7175788879394531},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6636674404144287},{"id":"https://openalex.org/keywords/component-analysis","display_name":"Component analysis","score":0.4761107861995697},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.394519180059433},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.36476409435272217},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.05489945411682129}],"concepts":[{"id":"https://openalex.org/C555745239","wikidata":"https://www.wikidata.org/wiki/Q133220","display_name":"Sonar","level":2,"score":0.8285725116729736},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.755916178226471},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.7175788879394531},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6636674404144287},{"id":"https://openalex.org/C2780692498","wikidata":"https://www.wikidata.org/wiki/Q16950721","display_name":"Component analysis","level":2,"score":0.4761107861995697},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.394519180059433},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36476409435272217},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.05489945411682129},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/newcas58973.2024.10666311","is_oa":false,"landing_page_url":"https://doi.org/10.1109/newcas58973.2024.10666311","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 22nd IEEE Interregional NEWCAS Conference (NEWCAS)","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":21,"referenced_works":["https://openalex.org/W2006129368","https://openalex.org/W2035611376","https://openalex.org/W2044893557","https://openalex.org/W2091432990","https://openalex.org/W2113958227","https://openalex.org/W2143396161","https://openalex.org/W2194775991","https://openalex.org/W2609317876","https://openalex.org/W2886938596","https://openalex.org/W2889047169","https://openalex.org/W2890401420","https://openalex.org/W2905172302","https://openalex.org/W2908214015","https://openalex.org/W2952773607","https://openalex.org/W2963941778","https://openalex.org/W2982703384","https://openalex.org/W3093726211","https://openalex.org/W3112170415","https://openalex.org/W3124953787","https://openalex.org/W3168082251","https://openalex.org/W7027716524"],"related_works":["https://openalex.org/W1971575144","https://openalex.org/W2369494890","https://openalex.org/W2139404519","https://openalex.org/W1513845058","https://openalex.org/W2121025724","https://openalex.org/W2393502243","https://openalex.org/W2123927273","https://openalex.org/W2897887562","https://openalex.org/W3112853371","https://openalex.org/W2370924545"],"abstract_inverted_index":{"Sonar":[0],"images":[1],"are":[2],"often":[3],"affected":[4],"by":[5,51,90],"reverberation,":[6],"posing":[7],"challenges":[8],"to":[9,37,106],"detecting":[10],"and":[11,21,118,127],"recognizing":[12],"target":[13],"signals.":[14],"This":[15],"paper":[16],"combines":[17],"the":[18,28,62,76,111,130,138,144],"strong":[19],"interpretability":[20],"controllability":[22],"of":[23,33,125,143],"signal":[24,140],"processing":[25],"model":[26],"with":[27,95],"powerful":[29],"data":[30],"mining":[31],"capability":[32,142],"deep":[34,48,78],"learning":[35,79],"technology":[36],"propose":[38],"a":[39],"novel":[40],"sonar":[41],"image":[42],"dereverberation":[43,71],"method.":[44,146],"Initially,":[45],"an":[46],"unsupervised":[47],"learning,":[49],"guided":[50],"domain":[52],"knowledge":[53,96],"from":[54,98],"Independent":[55],"Component":[56],"Analysis":[57],"(ICA),":[58],"is":[59],"employed":[60],"as":[61],"first-level":[63,66],"network.":[64],"The":[65],"called":[67,81],"ICANET,":[68],"enables":[69],"end-to-end":[70],"without":[72],"labeled":[73,92],"data.":[74],"Subsequently,":[75],"supervised":[77],"network":[80],"SKRNET":[82,109],"(Supervised":[83],"Knowledge-based":[84],"Reverberation":[85],"Network),":[86],"further":[87],"enhances":[88],"performance":[89],"incorporating":[91],"data,":[93,132],"along":[94],"obtained":[97],"ICANET.":[99],"Sea":[100],"trial":[101],"experiments":[102,135],"demonstrate":[103],"that":[104],"compared":[105],"benchmark":[107],"methods,":[108],"achieves":[110],"highest":[112],"signal-to-reverberation":[113],"ratios":[114],"(SRR)":[115],"at":[116,120],"35.76":[117],"entropy":[119],"7.39,":[121],"resulting":[122],"in":[123],"improvements":[124],"20.2%":[126],"4.8%":[128],"over":[129],"raw":[131],"respectively.":[133],"Simulation":[134],"also":[136],"confirm":[137],"superior":[139],"restoration":[141],"proposed":[145]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}