{"id":"https://openalex.org/W7128815718","doi":"https://doi.org/10.1109/access.2026.3664765","title":"A Generative Model for Cetacean Whistles Using Variational Autoencoder and Mixture of Gaussians","display_name":"A Generative Model for Cetacean Whistles Using Variational Autoencoder and Mixture of Gaussians","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7128815718","doi":"https://doi.org/10.1109/access.2026.3664765"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3664765","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3664765","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3664765","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125965621","display_name":"Geun-Ho Park","orcid":null},"institutions":[{"id":"https://openalex.org/I2801036362","display_name":"Agency for Defense Development","ror":"https://ror.org/05fhe0r85","country_code":"KR","type":"government","lineage":["https://openalex.org/I1327899338","https://openalex.org/I1344042128","https://openalex.org/I2801036362","https://openalex.org/I2801339556"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Geun-Ho Park","raw_affiliation_strings":["Agency for Defense Development, Jinhae, Changwon-si, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Agency for Defense Development, Jinhae, Changwon-si, Republic of Korea","institution_ids":["https://openalex.org/I2801036362"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jongmin Ahn","orcid":null},"institutions":[{"id":"https://openalex.org/I2801036362","display_name":"Agency for Defense Development","ror":"https://ror.org/05fhe0r85","country_code":"KR","type":"government","lineage":["https://openalex.org/I1327899338","https://openalex.org/I1344042128","https://openalex.org/I2801036362","https://openalex.org/I2801339556"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jongmin Ahn","raw_affiliation_strings":["Agency for Defense Development, Jinhae, Changwon-si, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Agency for Defense Development, Jinhae, Changwon-si, Republic of Korea","institution_ids":["https://openalex.org/I2801036362"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125914641","display_name":"Wanjin Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I2801036362","display_name":"Agency for Defense Development","ror":"https://ror.org/05fhe0r85","country_code":"KR","type":"government","lineage":["https://openalex.org/I1327899338","https://openalex.org/I1344042128","https://openalex.org/I2801036362","https://openalex.org/I2801339556"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Wanjin Kim","raw_affiliation_strings":["Agency for Defense Development, Jinhae, Changwon-si, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Agency for Defense Development, Jinhae, Changwon-si, Republic of Korea","institution_ids":["https://openalex.org/I2801036362"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017230964","display_name":"I. Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I2801036362","display_name":"Agency for Defense Development","ror":"https://ror.org/05fhe0r85","country_code":"KR","type":"government","lineage":["https://openalex.org/I1327899338","https://openalex.org/I1344042128","https://openalex.org/I2801036362","https://openalex.org/I2801339556"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Insoo Kim","raw_affiliation_strings":["Agency for Defense Development, Jinhae, Changwon-si, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Agency for Defense Development, Jinhae, Changwon-si, Republic of Korea","institution_ids":["https://openalex.org/I2801036362"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100370210","display_name":"Dong-Hun Lee","orcid":"https://orcid.org/0000-0003-1504-1385"},"institutions":[{"id":"https://openalex.org/I2801036362","display_name":"Agency for Defense Development","ror":"https://ror.org/05fhe0r85","country_code":"KR","type":"government","lineage":["https://openalex.org/I1327899338","https://openalex.org/I1344042128","https://openalex.org/I2801036362","https://openalex.org/I2801339556"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dong-Hun Lee","raw_affiliation_strings":["Agency for Defense Development, Jinhae, Changwon-si, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Agency for Defense Development, Jinhae, Changwon-si, Republic of Korea","institution_ids":["https://openalex.org/I2801036362"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5125965621"],"corresponding_institution_ids":["https://openalex.org/I2801036362"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.43204065,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"27877","last_page":"27894"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10659","display_name":"Marine animal studies overview","score":0.4341999888420105,"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"}},"topics":[{"id":"https://openalex.org/T10659","display_name":"Marine animal studies overview","score":0.4341999888420105,"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"}},{"id":"https://openalex.org/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.15189999341964722,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11698","display_name":"Underwater Acoustics Research","score":0.1459999978542328,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8445000052452087},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.7343000173568726},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6309000253677368},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.6126000285148621},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5898000001907349},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.5468000173568726},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.5236999988555908},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.43230000138282776},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.40610000491142273},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.39590001106262207}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8445000052452087},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.7343000173568726},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6388000249862671},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6309000253677368},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6189000010490417},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.6126000285148621},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5898000001907349},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.5468000173568726},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.5236999988555908},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.43230000138282776},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.40610000491142273},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.39590001106262207},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.39259999990463257},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3779999911785126},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.374099999666214},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.3677999973297119},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.3490999937057495},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.334199994802475},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.33390000462532043},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C56672385","wikidata":"https://www.wikidata.org/wiki/Q17157111","display_name":"Mixture distribution","level":3,"score":0.3294999897480011},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.31940001249313354},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.3122999966144562},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.2782999873161316},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27459999918937683},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C122770356","wikidata":"https://www.wikidata.org/wiki/Q1656753","display_name":"Identifiability","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C2779736610","wikidata":"https://www.wikidata.org/wiki/Q6884140","display_name":"Mixture theory","level":3,"score":0.25859999656677246}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3664765","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3664765","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3664765","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3664765","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Life below water","id":"https://metadata.un.org/sdg/14","score":0.6031518578529358}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,56,59,139],"tractable":[4],"and":[5,18,36,58,73,108,123,136,145],"compact":[6],"generative":[7],"model":[8],"for":[9],"cetacean":[10],"whistle":[11,51,70,92,110],"signals":[12,31],"based":[13],"on":[14],"Variational":[15],"Autoencoder":[16],"(VAE)":[17],"mixture":[19,60],"of":[20,50,61,77,91,101,141,151],"Gaussians":[21],"in":[22],"underwater":[23],"biomimetic":[24],"communication.":[25],"Cetacean":[26],"whistles":[27,122],"are":[28],"nonlinear":[29],"frequency-modulated":[30],"with":[32],"diverse":[33],"time-frequency":[34],"structures":[35],"can":[37],"be":[38],"interpreted":[39],"as":[40,96],"probability":[41,48],"density":[42,99],"functions.":[43],"To":[44],"learn":[45],"the":[46,74,78,88,120,124,130,149],"marginal":[47],"distribution":[49,76],"data,":[52],"this":[53],"study":[54],"incorporates":[55],"VAE":[57,64],"Gaussians.":[62],"The":[63,116],"learns":[65],"latent":[66,79,89],"space":[67,90],"representations":[68],"from":[69],"spectrogram":[71],"images,":[72],"prior":[75],"variables":[80],"is":[81,94],"modeled":[82,95],"using":[83,129],"multiple":[84],"Gaussian":[85,152],"components.":[86],"Since":[87],"contour":[93],"an":[97],"explicit":[98],"function":[100],"multimodal":[102],"structure,":[103],"it":[104],"enables":[105],"unsupervised":[106],"clustering":[107],"conditional":[109],"generation":[111],"by":[112],"analyzing":[113],"component-wise":[114],"features.":[115],"statistical":[117],"distance":[118],"between":[119],"synthesized":[121],"training":[125],"data":[126],"was":[127,154],"evaluated":[128],"Fr\u00e9chet":[131],"inception":[132],"distance,":[133],"Recall,":[134],"Precision,":[135],"F1-score":[137],"yielding":[138],"score":[140],"2.6767,":[142],"87.21%,":[143],"91.99%,":[144],"89.54%,":[146],"respectively,":[147],"when":[148],"number":[150],"components":[153],"set":[155],"to":[156],"10,000.":[157]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2026-02-14T00:00:00"}
