{"id":"https://openalex.org/W2560997814","doi":"https://doi.org/10.1109/iwcim.2016.7801190","title":"Recognition of vessel acoustic signatures using non-linear teager energy based features","display_name":"Recognition of vessel acoustic signatures using non-linear teager energy based features","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2560997814","doi":"https://doi.org/10.1109/iwcim.2016.7801190","mag":"2560997814"},"language":"en","primary_location":{"id":"doi:10.1109/iwcim.2016.7801190","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcim.2016.7801190","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)","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/A5110941179","display_name":"G\u00f6kmen Can","orcid":null},"institutions":[{"id":"https://openalex.org/I168864056","display_name":"Bilkent University","ror":"https://ror.org/02vh8a032","country_code":"TR","type":"education","lineage":["https://openalex.org/I168864056"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Gokmen Can","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Bilkent University, Bilkent, Ankara, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Bilkent University, Bilkent, Ankara, Turkey","institution_ids":["https://openalex.org/I168864056"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084345545","display_name":"Cem Emre Akba\u015f","orcid":null},"institutions":[{"id":"https://openalex.org/I168864056","display_name":"Bilkent University","ror":"https://ror.org/02vh8a032","country_code":"TR","type":"education","lineage":["https://openalex.org/I168864056"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Cem Emre Akbas","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Bilkent University, Bilkent, Ankara, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Bilkent University, Bilkent, Ankara, Turkey","institution_ids":["https://openalex.org/I168864056"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113644545","display_name":"A. Enis \u00c7etin","orcid":"https://orcid.org/0009-0009-7700-7124"},"institutions":[{"id":"https://openalex.org/I168864056","display_name":"Bilkent University","ror":"https://ror.org/02vh8a032","country_code":"TR","type":"education","lineage":["https://openalex.org/I168864056"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"A. Enis Cetin","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Bilkent University, Bilkent, Ankara, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Bilkent University, Bilkent, Ankara, Turkey","institution_ids":["https://openalex.org/I168864056"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I168864056"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11698","display_name":"Underwater Acoustics Research","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11698","display_name":"Underwater Acoustics Research","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9955999851226807,"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/T10659","display_name":"Marine animal studies overview","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.9174865484237671},{"id":"https://openalex.org/keywords/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.6765618920326233},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6491289138793945},{"id":"https://openalex.org/keywords/filter-bank","display_name":"Filter bank","score":0.6108797192573547},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6067936420440674},{"id":"https://openalex.org/keywords/energy-operator","display_name":"Energy operator","score":0.5927078127861023},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5501139163970947},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5397108793258667},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5246496200561523},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.498640775680542},{"id":"https://openalex.org/keywords/short-time-fourier-transform","display_name":"Short-time Fourier transform","score":0.49723389744758606},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4483538568019867},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.40427568554878235},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.2917023003101349},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.19162768125534058},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1518162190914154},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.09958833456039429}],"concepts":[{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.9174865484237671},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.6765618920326233},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6491289138793945},{"id":"https://openalex.org/C100515483","wikidata":"https://www.wikidata.org/wiki/Q3268235","display_name":"Filter bank","level":3,"score":0.6108797192573547},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6067936420440674},{"id":"https://openalex.org/C19579662","wikidata":"https://www.wikidata.org/wiki/Q461145","display_name":"Energy operator","level":3,"score":0.5927078127861023},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5501139163970947},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5397108793258667},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5246496200561523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.498640775680542},{"id":"https://openalex.org/C166386157","wikidata":"https://www.wikidata.org/wiki/Q1477735","display_name":"Short-time Fourier transform","level":4,"score":0.49723389744758606},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4483538568019867},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.40427568554878235},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.2917023003101349},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.19162768125534058},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1518162190914154},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.09958833456039429},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iwcim.2016.7801190","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwcim.2016.7801190","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)","raw_type":"proceedings-article"},{"id":"pmh:oai:repository.bilkent.edu.tr:11693/37718","is_oa":false,"landing_page_url":"http://hdl.handle.net/11693/37718","pdf_url":null,"source":{"id":"https://openalex.org/S4306400079","display_name":"Bilkent University Institutional Repository (Bilkent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I168864056","host_organization_name":"Bilkent University","host_organization_lineage":["https://openalex.org/I168864056"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2016","raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6100000143051147,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1542841509","https://openalex.org/W1973277164","https://openalex.org/W2074953333","https://openalex.org/W2128028443","https://openalex.org/W2132634821","https://openalex.org/W2162005634","https://openalex.org/W2401887077","https://openalex.org/W6632448044","https://openalex.org/W6633134201","https://openalex.org/W6679812875"],"related_works":["https://openalex.org/W1924444918","https://openalex.org/W2766680336","https://openalex.org/W2167033870","https://openalex.org/W1559551917","https://openalex.org/W2548564146","https://openalex.org/W2133320490","https://openalex.org/W4289830142","https://openalex.org/W1497065097","https://openalex.org/W2125446021","https://openalex.org/W3150393935"],"abstract_inverted_index":{"This":[0,149],"paper":[1],"proposes":[2],"a":[3,58,94,188],"vessel":[4,11,202],"recognition":[5,91],"and":[6,111,151],"classification":[7,97,189,200],"system":[8,92],"based":[9,18,45],"on":[10],"acoustic":[12,40,64,108],"signatures.":[13],"Teager":[14],"Energy":[15],"Operator":[16],"(TEO)":[17],"Mel":[19,119,134],"Frequency":[20],"Cepstral":[21],"Coefficients":[22],"(MFCC)":[23],"are":[24,145,153,173,185],"used":[25,116,186],"for":[26],"the":[27,39],"first":[28],"time":[29],"in":[30,51,199],"Underwater":[31],"Acoustic":[32],"Signal":[33],"Recognition":[34],"(UASR)":[35],"to":[36,73,86,102,117,165],"identify":[37],"platforms":[38],"noise":[41,65,161],"they":[42],"generate.":[43],"TEO":[44,99],"MFCC":[46],"(TEO-MFCC),":[47],"being":[48],"more":[49],"robust":[50],"noisy":[52],"conditions":[53],"than":[54],"conventional":[55,89],"MFCC,":[56],"provides":[57],"better":[59],"estimation":[60],"platform":[61],"energy.":[62],"Conventionally,":[63],"is":[66,85,100,115,125,139],"recognized":[67],"by":[68,77,127,176],"sonar":[69],"operators":[70],"who":[71],"listen":[72],"audio":[74],"signals":[75],"received":[76],"ship":[78],"sonars.":[79],"The":[80,122],"aim":[81],"of":[82,107,132,158,169,201],"this":[83,88],"work":[84],"replace":[87],"human-based":[90],"with":[93,156],"TEO-MFCC":[95,195],"features-based":[96],"system.":[98],"applied":[101],"short-time":[103],"Fourier":[104],"transform":[105,130],"(STFT)":[106],"signal":[109],"frames":[110],"Mel-scale":[112],"filter":[113],"bank":[114],"obtain":[118],"Teager-energy":[120,135],"spectrum.":[121,136],"feature":[123],"vector":[124],"constructed":[126],"discrete":[128],"cosine":[129],"(DCT)":[131],"logarithmic":[133],"Obtained":[137],"spectrum":[138],"transformed":[140],"into":[141],"cepstral":[142],"coefficients":[143],"that":[144,163,194],"labeled":[146],"as":[147,187],"TEO-MFCC.":[148],"analysis":[150],"implementation":[152],"carried":[154],"out":[155],"datasets":[157,172],"24":[159],"different":[160],"recordings":[162],"belong":[164],"10":[166],"separate":[167],"classes":[168],"vessels.":[170],"These":[171],"partially":[174],"provided":[175],"National":[177],"Park":[178],"Service":[179],"(NPS).":[180],"Artificial":[181],"Neural":[182],"Networks":[183],"(ANN)":[184],"method.":[190],"Experimental":[191],"results":[192],"demonstrate":[193],"achieves":[196],"99.5%":[197],"accuracy":[198],"noises.":[203]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}