{"id":"https://openalex.org/W3008952013","doi":"https://doi.org/10.1109/bigdata47090.2019.9005960","title":"Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks","display_name":"Classifying In-vehicle Noise from Multi-channel Sound Spectrum by Deep Beamforming Networks","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3008952013","doi":"https://doi.org/10.1109/bigdata47090.2019.9005960","mag":"3008952013"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9005960","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005960","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5112483522","display_name":"Seok-Jun Bu","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Seok-Jun Bu","raw_affiliation_strings":["Department of Computer Science, Yonsei University, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Yonsei University, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108081550","display_name":"Sung\u2010Bae Cho","orcid":"https://orcid.org/0000-0002-7027-2429"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sung-Bae Cho","raw_affiliation_strings":["Department of Computer Science, Yonsei University, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Yonsei University, Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112483522"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":0.8293,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.74702344,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3545","last_page":"3552"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9998000264167786,"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":0.9998000264167786,"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/T12759","display_name":"Vehicle Noise and Vibration Control","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/beamforming","display_name":"Beamforming","score":0.8602990508079529},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7282044887542725},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6712058782577515},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5840029120445251},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5101252198219299},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4898211658000946},{"id":"https://openalex.org/keywords/direction-of-arrival","display_name":"Direction of arrival","score":0.42730244994163513},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.4228455722332001},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40301766991615295},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3525582551956177},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.28072941303253174},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.24712195992469788},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.19653692841529846}],"concepts":[{"id":"https://openalex.org/C54197355","wikidata":"https://www.wikidata.org/wiki/Q5782992","display_name":"Beamforming","level":2,"score":0.8602990508079529},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7282044887542725},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6712058782577515},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5840029120445251},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5101252198219299},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4898211658000946},{"id":"https://openalex.org/C172051844","wikidata":"https://www.wikidata.org/wiki/Q5280438","display_name":"Direction of arrival","level":3,"score":0.42730244994163513},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.4228455722332001},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40301766991615295},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3525582551956177},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28072941303253174},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.24712195992469788},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.19653692841529846},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C21822782","wikidata":"https://www.wikidata.org/wiki/Q131214","display_name":"Antenna (radio)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9005960","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005960","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.550000011920929,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1603075283","https://openalex.org/W1839118408","https://openalex.org/W1993882792","https://openalex.org/W2051295922","https://openalex.org/W2060108923","https://openalex.org/W2099984896","https://openalex.org/W2102113734","https://openalex.org/W2143612262","https://openalex.org/W2144141949","https://openalex.org/W2155638623","https://openalex.org/W2167981995","https://openalex.org/W2186910770","https://openalex.org/W2187227936","https://openalex.org/W2398042854","https://openalex.org/W2398972335","https://openalex.org/W2404478433","https://openalex.org/W2407023693","https://openalex.org/W2463465753","https://openalex.org/W2471933213","https://openalex.org/W2559260703","https://openalex.org/W2636483419","https://openalex.org/W2746419079","https://openalex.org/W2806880137","https://openalex.org/W2897297418","https://openalex.org/W2944718693","https://openalex.org/W2962949934","https://openalex.org/W2963099423","https://openalex.org/W2963723765","https://openalex.org/W4285719527","https://openalex.org/W6638946568","https://openalex.org/W6675365184","https://openalex.org/W6686959547","https://openalex.org/W6762331631"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W3211292372","https://openalex.org/W803346624"],"abstract_inverted_index":{"Considering":[0],"the":[1,4,8,19,22,25,74,84,91,114],"trend":[2],"of":[3,21,47,55,119,130],"vehicle":[5,9],"market":[6],"where":[7],"becomes":[10],"quieter,":[11],"in-vehicle":[12,35,56],"rattling":[13,36,48,120],"noise":[14,49,121],"is":[15,111,122],"significant":[16],"criterion":[17],"for":[18,33,117,124],"quality":[20],"vehicle.":[23],"Though":[24],"latest":[26],"deep":[27,93],"learning":[28,94],"algorithms":[29,95],"have":[30],"been":[31],"introduced":[32],"classifying":[34],"noise,":[37],"there":[38],"are":[39],"limitations":[40],"due":[41],"to":[42,82],"impulsive":[43],"and":[44,50,52,99,105],"transient":[45],"nature":[46],"reflective":[51],"refractive":[53],"characteristics":[54],"environment.":[57],"In":[58,108],"this":[59],"paper,":[60],"we":[61],"propose":[62],"a":[63,128],"novel":[64],"beamforming":[65,76],"method":[66,89],"that":[67,113],"extracts":[68],"intra-interchannel":[69],"spatial":[70],"features":[71],"by":[72,101],"parameterizing":[73],"optimal":[75],"weights":[77],"including":[78],"Direction-of-Arrival":[79],"(DOA)":[80],"function":[81],"overcome":[83],"addressed":[85],"problem.":[86],"The":[87],"proposed":[88],"outperformed":[90],"existing":[92],"with":[96],"0.9270":[97],"accuracy":[98],"verified":[100],"10-fold":[102],"cross":[103],"validation":[104],"chi-squared":[106],"test.":[107],"addition,":[109],"it":[110],"shown":[112],"time":[115],"cost":[116],"classification":[118,126],"appropriate":[123],"real-time":[125],"as":[127],"side-effect":[129],"using":[131],"convolution-pooling":[132],"operations.":[133]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}