{"id":"https://openalex.org/W2899429527","doi":"https://doi.org/10.1109/mlsp.2018.8516968","title":"ANALYSING REPLAY SPOOFING COUNTERMEASURE PERFORMANCE UNDER VARIED CONDITIONS","display_name":"ANALYSING REPLAY SPOOFING COUNTERMEASURE PERFORMANCE UNDER VARIED CONDITIONS","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2899429527","doi":"https://doi.org/10.1109/mlsp.2018.8516968","mag":"2899429527"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2018.8516968","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2018.8516968","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/49725/1/Benetos%20Analysing%20replay%20spoofing%202018%20Accepted.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018585368","display_name":"Bhusan Chettri","orcid":"https://orcid.org/0000-0002-6892-7588"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Bhusan Chettri","raw_affiliation_strings":["School of EECS, Queen Mary University of London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"School of EECS, Queen Mary University of London, United Kingdom","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054217723","display_name":"Bob L. Sturm","orcid":"https://orcid.org/0000-0003-2549-6367"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Bob L. Sturm","raw_affiliation_strings":["School of EECS, KTH Royal Institute of Engineering, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"School of EECS, KTH Royal Institute of Engineering, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084672392","display_name":"Emmanouil Benetos","orcid":null},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Emmanouil Benetos","raw_affiliation_strings":["School of EECS, Queen Mary University of London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"School of EECS, Queen Mary University of London, United Kingdom","institution_ids":["https://openalex.org/I166337079"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5018585368"],"corresponding_institution_ids":["https://openalex.org/I166337079"],"apc_list":null,"apc_paid":null,"fwci":1.0153,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.83109643,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"9","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9994999766349792,"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":0.9994999766349792,"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/T11309","display_name":"Music and Audio Processing","score":0.9959999918937683,"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/T10860","display_name":"Speech and Audio Processing","score":0.9857000112533569,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8455492258071899},{"id":"https://openalex.org/keywords/spoofing-attack","display_name":"Spoofing attack","score":0.8062992691993713},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.7500065565109253},{"id":"https://openalex.org/keywords/replay-attack","display_name":"Replay attack","score":0.7494233846664429},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6121679544448853},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.546606719493866},{"id":"https://openalex.org/keywords/unavailability","display_name":"Unavailability","score":0.544224739074707},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.5200712084770203},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4870198369026184},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.44299110770225525},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4401251971721649},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4249010682106018},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.26061177253723145},{"id":"https://openalex.org/keywords/authentication","display_name":"Authentication (law)","score":0.12634405493736267}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8455492258071899},{"id":"https://openalex.org/C167900197","wikidata":"https://www.wikidata.org/wiki/Q11081100","display_name":"Spoofing attack","level":2,"score":0.8062992691993713},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.7500065565109253},{"id":"https://openalex.org/C11560541","wikidata":"https://www.wikidata.org/wiki/Q1756025","display_name":"Replay attack","level":3,"score":0.7494233846664429},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6121679544448853},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.546606719493866},{"id":"https://openalex.org/C2780505938","wikidata":"https://www.wikidata.org/wiki/Q17093282","display_name":"Unavailability","level":2,"score":0.544224739074707},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5200712084770203},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4870198369026184},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.44299110770225525},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4401251971721649},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4249010682106018},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26061177253723145},{"id":"https://openalex.org/C148417208","wikidata":"https://www.wikidata.org/wiki/Q4825882","display_name":"Authentication (law)","level":2,"score":0.12634405493736267},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/mlsp.2018.8516968","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2018.8516968","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:qmro.qmul.ac.uk:123456789/49725","is_oa":true,"landing_page_url":"http://qmro.qmul.ac.uk/xmlui/handle/123456789/49725","pdf_url":"https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/49725/1/Benetos%20Analysing%20replay%20spoofing%202018%20Accepted.pdf","source":{"id":"https://openalex.org/S4306400530","display_name":"Queen Mary Research Online (Queen Mary University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"}],"best_oa_location":{"id":"pmh:oai:qmro.qmul.ac.uk:123456789/49725","is_oa":true,"landing_page_url":"http://qmro.qmul.ac.uk/xmlui/handle/123456789/49725","pdf_url":"https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/49725/1/Benetos%20Analysing%20replay%20spoofing%202018%20Accepted.pdf","source":{"id":"https://openalex.org/S4306400530","display_name":"Queen Mary Research Online (Queen Mary University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2899429527.pdf","grobid_xml":"https://content.openalex.org/works/W2899429527.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1586475397","https://openalex.org/W1688416049","https://openalex.org/W1980581462","https://openalex.org/W2062703747","https://openalex.org/W2069883713","https://openalex.org/W2101234009","https://openalex.org/W2123299109","https://openalex.org/W2148154194","https://openalex.org/W2150769028","https://openalex.org/W2170088868","https://openalex.org/W2290028669","https://openalex.org/W2344822617","https://openalex.org/W2398971481","https://openalex.org/W2403139431","https://openalex.org/W2407170210","https://openalex.org/W2585365199","https://openalex.org/W2745744274","https://openalex.org/W2745896134","https://openalex.org/W2747024632","https://openalex.org/W2749090340","https://openalex.org/W2802820526","https://openalex.org/W2803805378","https://openalex.org/W2807325376","https://openalex.org/W2964121744","https://openalex.org/W3198123200","https://openalex.org/W6631190155","https://openalex.org/W6635137453","https://openalex.org/W6637209432","https://openalex.org/W6675354045","https://openalex.org/W6696494075","https://openalex.org/W6704529918","https://openalex.org/W6712618806","https://openalex.org/W6713385238","https://openalex.org/W6713566751","https://openalex.org/W6751780174"],"related_works":["https://openalex.org/W2547760228","https://openalex.org/W3007059209","https://openalex.org/W4220832730","https://openalex.org/W4313224733","https://openalex.org/W1688416049","https://openalex.org/W4312120756","https://openalex.org/W2972017669","https://openalex.org/W2783658022","https://openalex.org/W2836844603","https://openalex.org/W3173366380"],"abstract_inverted_index":{"In":[0],"this":[1,55],"paper,":[2],"we":[3,57,89],"aim":[4],"to":[5,85,96,133],"understand":[6,97],"what":[7],"makes":[8],"replay":[9,73,81,116],"spoofing":[10,117],"detection":[11],"difficult":[12,132],"in":[13,83,160],"the":[14,17,86,98,122,136,139,154,161],"context":[15],"of":[16,100,114,124,138,156,165],"ASVspoof":[18],"2017":[19],"corpus.":[20],"We":[21],"use":[22],"FFT":[23],"spectra,":[24],"mel":[25],"frequency":[26],"cepstral":[27],"coefficients":[28],"(MFCC)":[29],"and":[30,35,49,109,143,145],"inverted":[31],"MFCC":[32],"(IMFCC)":[33],"frontends":[34],"investigate":[36],"different":[37],"backends":[38],"based":[39,62],"on":[40],"Convolutional":[41],"Neural":[42],"Networks":[43],"(CNNs),":[44],"Gaussian":[45],"Mixture":[46],"Models":[47],"(GMMs)":[48],"Support":[50],"Vector":[51],"Machines":[52],"(SVMs).":[53],"On":[54],"database,":[56],"find":[58,90],"that":[59,91,153],"IMFCC":[60],"frontend":[61],"systems":[63],"show":[64],"smaller":[65],"equal":[66],"error":[67],"rate":[68],"(EER)":[69],"for":[70,78,126,135],"high":[71],"quality":[72,80],"attacks":[74,82],"but":[75],"higher":[76],"EER":[77],"low":[79],"comparison":[84],"baseline.":[87],"However,":[88],"it":[92,130],"is":[93,121,131],"not":[94],"straightforward":[95],"influence":[99,172],"an":[101],"acoustic":[102],"environment":[103],"(AE),":[104],"a":[105,110,115],"playback":[106],"device":[107,112],"(PD)":[108],"recording":[111],"(RD)":[113],"attack.":[118],"One":[119],"reason":[120],"unavailability":[123],"metadata":[125],"genuine":[127,166],"recordings.":[128],"Second,":[129],"account":[134],"effects":[137],"factors:":[140],"AE,":[141],"PD":[142],"RD,":[144],"their":[146],"interactions.":[147],"Finally,":[148],"our":[149],"frame-level":[150],"analysis":[151],"shows":[152],"presence":[155],"cues":[157],"(recording":[158],"artefacts)":[159],"first":[162],"few":[163],"frames":[164],"signals":[167],"(missing":[168],"from":[169],"replayed":[170],"ones)":[171],"class":[173],"prediction.":[174]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
