{"id":"https://openalex.org/W4376480427","doi":"https://doi.org/10.1109/access.2023.3275790","title":"Voice Spoofing Detection Through Residual Network, Max Feature Map, and Depthwise Separable Convolution","display_name":"Voice Spoofing Detection Through Residual Network, Max Feature Map, and Depthwise Separable Convolution","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4376480427","doi":"https://doi.org/10.1109/access.2023.3275790"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3275790","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3275790","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10123935.pdf","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://ieeexplore.ieee.org/ielx7/6287639/10005208/10123935.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014457836","display_name":"Il\u2010Youp Kwak","orcid":"https://orcid.org/0000-0002-7117-7669"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Il-Youp Kwak","raw_affiliation_strings":["Department of Applied Statistics, Chung-Ang University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-7117-7669","affiliations":[{"raw_affiliation_string":"Department of Applied Statistics, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025121703","display_name":"Sungsu Kwag","orcid":"https://orcid.org/0009-0007-6912-6452"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sungsu Kwag","raw_affiliation_strings":["Samsung Research, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0009-0007-6912-6452","affiliations":[{"raw_affiliation_string":"Samsung Research, Seoul, South Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100748045","display_name":"Junhee Lee","orcid":"https://orcid.org/0009-0000-4292-1973"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Junhee Lee","raw_affiliation_strings":["Samsung Research, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0009-0000-4292-1973","affiliations":[{"raw_affiliation_string":"Samsung Research, Seoul, South Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052644430","display_name":"Youngbae Jeon","orcid":"https://orcid.org/0000-0002-4628-6345"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngbae Jeon","raw_affiliation_strings":["Samsung Research, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Research, Seoul, South Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049040331","display_name":"Jeonghwan Hwang","orcid":"https://orcid.org/0000-0003-4399-1556"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jeonghwan Hwang","raw_affiliation_strings":["School of Cybersecurity, Korea University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Cybersecurity, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102414352","display_name":"Hyo\u2010Jung Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyo-Jung Choi","raw_affiliation_strings":["Department of Applied Statistics, Chung-Ang University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Statistics, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018493626","display_name":"Jonghoon Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jong-Hoon Yang","raw_affiliation_strings":["Department of Applied Statistics, Chung-Ang University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Statistics, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":null,"display_name":"So-Yul Han","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"So-Yul Han","raw_affiliation_strings":["Department of Applied Statistics, Chung-Ang University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Statistics, Chung-Ang University, Seoul, South Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084089956","display_name":"Jun Ho Huh","orcid":"https://orcid.org/0000-0003-2007-4018"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jun Ho Huh","raw_affiliation_strings":["Samsung Research, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-2007-4018","affiliations":[{"raw_affiliation_string":"Samsung Research, Seoul, South Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021321193","display_name":"Choong-Hoon Lee","orcid":"https://orcid.org/0000-0001-5146-0259"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Choong-Hoon Lee","raw_affiliation_strings":["Samsung Research, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-5146-0259","affiliations":[{"raw_affiliation_string":"Samsung Research, Seoul, South Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037589971","display_name":"Ji Won Yoon","orcid":"https://orcid.org/0000-0003-2123-9849"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ji Won Yoon","raw_affiliation_strings":["School of Cybersecurity, Korea University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-2123-9849","affiliations":[{"raw_affiliation_string":"School of Cybersecurity, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":11,"corresponding_author_ids":["https://openalex.org/A5014457836"],"corresponding_institution_ids":["https://openalex.org/I67900169"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.5276,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":{"value":0.91254293,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"11","issue":null,"first_page":"49140","last_page":"49152"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9984999895095825,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9984999895095825,"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/T10860","display_name":"Speech and Audio Processing","score":0.9948999881744385,"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/T11448","display_name":"Face recognition and analysis","score":0.9872999787330627,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7756816148757935},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7494024038314819},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7197282910346985},{"id":"https://openalex.org/keywords/separable-space","display_name":"Separable space","score":0.6797972321510315},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.628544807434082},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6178851127624512},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.570231556892395},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4374625086784363},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3417418897151947},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3134099245071411},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24452295899391174},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14096122980117798}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7756816148757935},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7494024038314819},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7197282910346985},{"id":"https://openalex.org/C70710897","wikidata":"https://www.wikidata.org/wiki/Q680081","display_name":"Separable space","level":2,"score":0.6797972321510315},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.628544807434082},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6178851127624512},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.570231556892395},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4374625086784363},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3417418897151947},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3134099245071411},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24452295899391174},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14096122980117798},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3275790","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3275790","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10123935.pdf","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"},{"id":"pmh:oai:doaj.org/article:2a963012ecda48229db2687f40edbff2","is_oa":true,"landing_page_url":"https://doaj.org/article/2a963012ecda48229db2687f40edbff2","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 49140-49152 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3275790","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3275790","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10123935.pdf","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":"Partnerships for the goals","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/17"}],"awards":[{"id":"https://openalex.org/G3984847229","display_name":null,"funder_award_id":"RS-2023-00208284","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321202","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4376480427.pdf","grobid_xml":"https://content.openalex.org/works/W4376480427.grobid-xml"},"referenced_works_count":57,"referenced_works":["https://openalex.org/W167016754","https://openalex.org/W1522301498","https://openalex.org/W1533861849","https://openalex.org/W2176804518","https://openalex.org/W2295107390","https://openalex.org/W2531409750","https://openalex.org/W2590129515","https://openalex.org/W2657631929","https://openalex.org/W2745896134","https://openalex.org/W2747024632","https://openalex.org/W2765636281","https://openalex.org/W2786672974","https://openalex.org/W2794793454","https://openalex.org/W2799053639","https://openalex.org/W2809925683","https://openalex.org/W2811004397","https://openalex.org/W2889326414","https://openalex.org/W2889361425","https://openalex.org/W2936802426","https://openalex.org/W2962858109","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2963460857","https://openalex.org/W2963508548","https://openalex.org/W2963609956","https://openalex.org/W2963691546","https://openalex.org/W2964002811","https://openalex.org/W2964243274","https://openalex.org/W2972594541","https://openalex.org/W2972811785","https://openalex.org/W2972884023","https://openalex.org/W2972909277","https://openalex.org/W2973164265","https://openalex.org/W2973181078","https://openalex.org/W3003838795","https://openalex.org/W3036601975","https://openalex.org/W3081618117","https://openalex.org/W3105920175","https://openalex.org/W3163596559","https://openalex.org/W3163694882","https://openalex.org/W3199161700","https://openalex.org/W3201773091","https://openalex.org/W4210849719","https://openalex.org/W4221138880","https://openalex.org/W4225919806","https://openalex.org/W4226264925","https://openalex.org/W4297775537","https://openalex.org/W4298394377","https://openalex.org/W4300824008","https://openalex.org/W6606837198","https://openalex.org/W6631190155","https://openalex.org/W6631943919","https://openalex.org/W6737664043","https://openalex.org/W6748816842","https://openalex.org/W6749489859","https://openalex.org/W6761712280","https://openalex.org/W6780218876"],"related_works":["https://openalex.org/W2009525028","https://openalex.org/W2530952058","https://openalex.org/W2972212393","https://openalex.org/W2582836483","https://openalex.org/W4299366318","https://openalex.org/W3158431807","https://openalex.org/W4297580547","https://openalex.org/W3088721469","https://openalex.org/W2951583185","https://openalex.org/W4308155352"],"abstract_inverted_index":{"The":[0,58],"goal":[1],"of":[2,30,60,137,163,196,212],"the":[3,42,45,55,61,65,121,140,152,157,210],"\u201c2019":[4],"Automatic":[5],"Speaker":[6],"Verification":[7],"Spoofing":[8],"and":[9,35,103,115],"Countermeasures":[10],"Challenge\u201d":[11],"(ASVspoof)":[12],"was":[13],"to":[14,18,78,110,173,189,202,209],"make":[15],"it":[16],"easier":[17],"create":[19,111],"systems":[20],"that":[21,47,71],"could":[22],"identify":[23],"voice":[24,90],"spoofing":[25],"attacks":[26],"with":[27,85],"high":[28],"levels":[29],"accuracy.":[31,81],"However,":[32],"model":[33,127,175],"complexity":[34],"latency":[36],"requirements":[37,51],"were":[38],"not":[39],"emphasized":[40],"in":[41,54,156,179,184],"competition,":[43,158],"despite":[44],"fact":[46],"they":[48],"are":[49],"stringent":[50],"for":[52,89],"implementation":[53],"real":[56],"world.":[57],"majority":[59],"top-performing":[62],"solutions":[63],"from":[64],"competition":[66],"used":[67,200],"an":[68,144,161,180],"ensemble":[69,154],"technique":[70],"merged":[72,98],"numerous":[73],"sophisticated":[74],"deep":[75],"learning":[76],"models":[77],"maximize":[79],"detection":[80,132,211],"Those":[82],"approaches":[83],"struggle":[84],"real-world":[86],"deployment":[87],"restrictions":[88],"assistants":[91],"which":[92,159,204],"would":[93],"have":[94],"restricted":[95],"resources.":[96],"We":[97,165],"skip":[99],"connection":[100],"(from":[101,107,171,187],"ResNet)":[102],"max":[104],"feature":[105],"map":[106],"Light":[108],"CNN)":[109],"a":[112,129],"compact":[113],"system,":[114],"we":[116,199],"tested":[117],"its":[118],"performance":[119,194],"using":[120,143,167],"ASVspoof":[122],"2019":[123],"dataset.":[124],"Our":[125],"single":[126],"achieved":[128],"replay":[130],"attack":[131],"equal":[133],"error":[134],"rate":[135],"(EER)":[136],"0.30%":[138],"on":[139],"evaluation":[141],"set":[142],"optimized":[145],"constant":[146],"Q":[147],"transform":[148],"(CQT)":[149],"feature,":[150],"outperforming":[151],"top":[153],"system":[155],"scored":[160],"EER":[162],"0.39%.":[164],"experimented":[166],"depthwise":[168],"separable":[169],"convolutions":[170],"MobileNet)":[172],"reduce":[174],"sizes;":[176],"this":[177],"resulted":[178],"84.3":[181],"percent":[182],"reduction":[183],"parameter":[185],"count":[186],"286K":[188],"45K),":[190],"while":[191],"maintaining":[192],"similar":[193],"(EER":[195],"0.36%).":[197],"Additionally,":[198],"Grad-CAM":[201],"clarify":[203],"spectrogram":[205],"regions":[206],"significantly":[207],"contribute":[208],"fake":[213],"data.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
