{"id":"https://openalex.org/W2903442965","doi":"https://doi.org/10.23919/eusipco.2018.8553480","title":"Anomaly Detection Based on Feature Reconstruction from Subsampled Audio Signals","display_name":"Anomaly Detection Based on Feature Reconstruction from Subsampled Audio Signals","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2903442965","doi":"https://doi.org/10.23919/eusipco.2018.8553480","mag":"2903442965"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco.2018.8553480","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2018.8553480","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 26th European Signal Processing Conference (EUSIPCO)","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/A5052394367","display_name":"Yohei Kawaguchi","orcid":"https://orcid.org/0000-0002-2329-5441"},"institutions":[{"id":"https://openalex.org/I65143321","display_name":"Hitachi (Japan)","ror":"https://ror.org/02exqgm79","country_code":"JP","type":"company","lineage":["https://openalex.org/I65143321"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yohei Kawaguchi","raw_affiliation_strings":["Research and Development Group, Hitachi, Ltd., Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Research and Development Group, Hitachi, Ltd., Tokyo, Japan","institution_ids":["https://openalex.org/I65143321"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5052394367"],"corresponding_institution_ids":["https://openalex.org/I65143321"],"apc_list":null,"apc_paid":null,"fwci":1.1402,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.8422908,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2524","last_page":"2528"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.992900013923645,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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":0.7210806012153625},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.6825896501541138},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6612520217895508},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6256098747253418},{"id":"https://openalex.org/keywords/signal-reconstruction","display_name":"Signal reconstruction","score":0.6014835834503174},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5940295457839966},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5921947956085205},{"id":"https://openalex.org/keywords/nyquist\u2013shannon-sampling-theorem","display_name":"Nyquist\u2013Shannon sampling theorem","score":0.5514984726905823},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4999964237213135},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.48664984107017517},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4790988266468048},{"id":"https://openalex.org/keywords/nyquist-rate","display_name":"Nyquist rate","score":0.4676263928413391},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.4184371829032898},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.31755977869033813},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.24362239241600037},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.06933721899986267}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7210806012153625},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.6825896501541138},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6612520217895508},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6256098747253418},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.6014835834503174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5940295457839966},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5921947956085205},{"id":"https://openalex.org/C288623","wikidata":"https://www.wikidata.org/wiki/Q679800","display_name":"Nyquist\u2013Shannon sampling theorem","level":2,"score":0.5514984726905823},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4999964237213135},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.48664984107017517},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4790988266468048},{"id":"https://openalex.org/C65914096","wikidata":"https://www.wikidata.org/wiki/Q6273772","display_name":"Nyquist rate","level":4,"score":0.4676263928413391},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.4184371829032898},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.31755977869033813},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.24362239241600037},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.06933721899986267},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"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/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco.2018.8553480","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2018.8553480","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 26th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1567217660","https://openalex.org/W1601124178","https://openalex.org/W1650484478","https://openalex.org/W1836465849","https://openalex.org/W2033359964","https://openalex.org/W2095705004","https://openalex.org/W2108287924","https://openalex.org/W2127979711","https://openalex.org/W2140774607","https://openalex.org/W2160335114","https://openalex.org/W2194345886","https://openalex.org/W2211589247","https://openalex.org/W2402302915","https://openalex.org/W2542170364","https://openalex.org/W2546302380","https://openalex.org/W2771164042","https://openalex.org/W2902455138","https://openalex.org/W6638667902","https://openalex.org/W6674330103","https://openalex.org/W6680824009","https://openalex.org/W6756753118"],"related_works":["https://openalex.org/W2210983845","https://openalex.org/W2054047433","https://openalex.org/W2119250743","https://openalex.org/W2545081564","https://openalex.org/W2618908398","https://openalex.org/W4245974431","https://openalex.org/W2096995356","https://openalex.org/W2292746209","https://openalex.org/W2906069382","https://openalex.org/W3001645640"],"abstract_inverted_index":{"We":[0],"aim":[1],"to":[2],"reduce":[3],"the":[4,14,47,62,79,86,95,99,114],"cost":[5],"of":[6,78,98],"sound":[7],"monitoring":[8],"for":[9,31,43,57,73,110],"maintain":[10],"machinery":[11,33],"by":[12],"reducing":[13],"sampling":[15,24,32],"rate,":[16],"i.e.,":[17],"sub-Nyquist":[18,23],"sampling.":[19],"Monitoring":[20],"based":[21],"on":[22],"requires":[25],"two":[26],"sub-systems:":[27],"a":[28,36,40,53,68],"sub-system":[29,41],"on-site":[30],"sounds":[34],"at":[35],"low":[37],"rate":[38],"and":[39,93],"off-site":[42],"detecting":[44],"anomalies":[45],"from":[46,61,113],"subsampled":[48,63,87,115],"signal.":[49,64,101,116],"This":[50],"paper":[51],"proposes":[52],"feature":[54,96],"reconstruction":[55],"method":[56,66,107],"enabling":[58],"anomaly":[59,111],"detection":[60,112],"The":[65,76],"applies":[67],"long":[69],"short-term":[70],"memory-(LSTM)-based":[71],"network":[72,81],"reconstructing":[74],"features.":[75],"novelty":[77],"proposed":[80],"is":[82,108],"that":[83,105],"it":[84],"receives":[85],"time-domain":[88],"signal":[89],"as":[90],"input":[91],"directly":[92],"reconstructs":[94],"vector":[97],"original":[100],"Experimental":[102],"results":[103],"indicate":[104],"our":[106],"suitable":[109]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
