On the performance of empirical mode decomposition-based replay spoofing detection in speaker verification systems

Abstract Automatic speaker verification (ASV) systems have maximum threat from replay spoofing attacks. High frequency regions of the underlying audio signal exhibit the phenomenon about their presence. It is therefore useful to decompose the underlying audio signal into frequency bands or regions f...
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Autor*in:	Mankad, Sapan H. [verfasserIn] Garg, Sanjay [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Automatic speaker verification Replay spoofing Antispoofing Empirical mode decomposition Countermeasures

Übergeordnetes Werk:	Enthalten in: Progress in artificial intelligence - Berlin : Springer, 2012, 9(2020), 4 vom: 29. Aug., Seite 325-339
Übergeordnetes Werk:	volume:9 ; year:2020 ; number:4 ; day:29 ; month:08 ; pages:325-339

Links:	Volltext

DOI / URN:	10.1007/s13748-020-00216-0

Katalog-ID:	SPR041735781

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520			\|a Abstract Automatic speaker verification (ASV) systems have maximum threat from replay spoofing attacks. High frequency regions of the underlying audio signal exhibit the phenomenon about their presence. It is therefore useful to decompose the underlying audio signal into frequency bands or regions for possible analysis. In this paper, an empirical mode decomposition (EMD)-based replay spoofing detection system is presented. Using EMD, each signal is decomposed into several monotonic intrinsic mode functions (IMFs). The signal is reconstructed and represented using one or more subsets of these IMFs by performing different combinations for spoofing detection. Results on ASVspoof 2017 version 2.0 and AVspoof benchmark replay attack datasets indicate that there is a potential in initial IMFs to carry replay attack patterns, and that is sufficient rather than processing the entire signal. The proposed approach can also serve as a preprocessing technique by employing dimension reduction strategy. Cross-corpus experiments on the systems indicate the limitations of ASV antispoofing systems due to mismatched conditions.
650		4	\|a Automatic speaker verification \|7 (dpeaa)DE-He213
650		4	\|a Replay spoofing \|7 (dpeaa)DE-He213
650		4	\|a Antispoofing \|7 (dpeaa)DE-He213
650		4	\|a Empirical mode decomposition \|7 (dpeaa)DE-He213
650		4	\|a Countermeasures \|7 (dpeaa)DE-He213
700	1		\|a Garg, Sanjay \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Progress in artificial intelligence \|d Berlin : Springer, 2012 \|g 9(2020), 4 vom: 29. Aug., Seite 325-339 \|w (DE-627)718730933 \|w (DE-600)2668413-5 \|x 2192-6360 \|7 nnns
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publishDate	2020
allfields	10.1007/s13748-020-00216-0 doi (DE-627)SPR041735781 (SPR)s13748-020-00216-0-e DE-627 ger DE-627 rakwb eng 004 600 ASE Mankad, Sapan H. verfasserin aut On the performance of empirical mode decomposition-based replay spoofing detection in speaker verification systems 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Automatic speaker verification (ASV) systems have maximum threat from replay spoofing attacks. High frequency regions of the underlying audio signal exhibit the phenomenon about their presence. It is therefore useful to decompose the underlying audio signal into frequency bands or regions for possible analysis. In this paper, an empirical mode decomposition (EMD)-based replay spoofing detection system is presented. Using EMD, each signal is decomposed into several monotonic intrinsic mode functions (IMFs). The signal is reconstructed and represented using one or more subsets of these IMFs by performing different combinations for spoofing detection. Results on ASVspoof 2017 version 2.0 and AVspoof benchmark replay attack datasets indicate that there is a potential in initial IMFs to carry replay attack patterns, and that is sufficient rather than processing the entire signal. The proposed approach can also serve as a preprocessing technique by employing dimension reduction strategy. Cross-corpus experiments on the systems indicate the limitations of ASV antispoofing systems due to mismatched conditions. Automatic speaker verification (dpeaa)DE-He213 Replay spoofing (dpeaa)DE-He213 Antispoofing (dpeaa)DE-He213 Empirical mode decomposition (dpeaa)DE-He213 Countermeasures (dpeaa)DE-He213 Garg, Sanjay verfasserin aut Enthalten in Progress in artificial intelligence Berlin : Springer, 2012 9(2020), 4 vom: 29. Aug., Seite 325-339 (DE-627)718730933 (DE-600)2668413-5 2192-6360 nnns volume:9 year:2020 number:4 day:29 month:08 pages:325-339 https://dx.doi.org/10.1007/s13748-020-00216-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2020 4 29 08 325-339
spelling	10.1007/s13748-020-00216-0 doi (DE-627)SPR041735781 (SPR)s13748-020-00216-0-e DE-627 ger DE-627 rakwb eng 004 600 ASE Mankad, Sapan H. verfasserin aut On the performance of empirical mode decomposition-based replay spoofing detection in speaker verification systems 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Automatic speaker verification (ASV) systems have maximum threat from replay spoofing attacks. High frequency regions of the underlying audio signal exhibit the phenomenon about their presence. It is therefore useful to decompose the underlying audio signal into frequency bands or regions for possible analysis. In this paper, an empirical mode decomposition (EMD)-based replay spoofing detection system is presented. Using EMD, each signal is decomposed into several monotonic intrinsic mode functions (IMFs). The signal is reconstructed and represented using one or more subsets of these IMFs by performing different combinations for spoofing detection. Results on ASVspoof 2017 version 2.0 and AVspoof benchmark replay attack datasets indicate that there is a potential in initial IMFs to carry replay attack patterns, and that is sufficient rather than processing the entire signal. The proposed approach can also serve as a preprocessing technique by employing dimension reduction strategy. Cross-corpus experiments on the systems indicate the limitations of ASV antispoofing systems due to mismatched conditions. Automatic speaker verification (dpeaa)DE-He213 Replay spoofing (dpeaa)DE-He213 Antispoofing (dpeaa)DE-He213 Empirical mode decomposition (dpeaa)DE-He213 Countermeasures (dpeaa)DE-He213 Garg, Sanjay verfasserin aut Enthalten in Progress in artificial intelligence Berlin : Springer, 2012 9(2020), 4 vom: 29. Aug., Seite 325-339 (DE-627)718730933 (DE-600)2668413-5 2192-6360 nnns volume:9 year:2020 number:4 day:29 month:08 pages:325-339 https://dx.doi.org/10.1007/s13748-020-00216-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2020 4 29 08 325-339
allfields_unstemmed	10.1007/s13748-020-00216-0 doi (DE-627)SPR041735781 (SPR)s13748-020-00216-0-e DE-627 ger DE-627 rakwb eng 004 600 ASE Mankad, Sapan H. verfasserin aut On the performance of empirical mode decomposition-based replay spoofing detection in speaker verification systems 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Automatic speaker verification (ASV) systems have maximum threat from replay spoofing attacks. High frequency regions of the underlying audio signal exhibit the phenomenon about their presence. It is therefore useful to decompose the underlying audio signal into frequency bands or regions for possible analysis. In this paper, an empirical mode decomposition (EMD)-based replay spoofing detection system is presented. Using EMD, each signal is decomposed into several monotonic intrinsic mode functions (IMFs). The signal is reconstructed and represented using one or more subsets of these IMFs by performing different combinations for spoofing detection. Results on ASVspoof 2017 version 2.0 and AVspoof benchmark replay attack datasets indicate that there is a potential in initial IMFs to carry replay attack patterns, and that is sufficient rather than processing the entire signal. The proposed approach can also serve as a preprocessing technique by employing dimension reduction strategy. Cross-corpus experiments on the systems indicate the limitations of ASV antispoofing systems due to mismatched conditions. Automatic speaker verification (dpeaa)DE-He213 Replay spoofing (dpeaa)DE-He213 Antispoofing (dpeaa)DE-He213 Empirical mode decomposition (dpeaa)DE-He213 Countermeasures (dpeaa)DE-He213 Garg, Sanjay verfasserin aut Enthalten in Progress in artificial intelligence Berlin : Springer, 2012 9(2020), 4 vom: 29. Aug., Seite 325-339 (DE-627)718730933 (DE-600)2668413-5 2192-6360 nnns volume:9 year:2020 number:4 day:29 month:08 pages:325-339 https://dx.doi.org/10.1007/s13748-020-00216-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2020 4 29 08 325-339
allfieldsGer	10.1007/s13748-020-00216-0 doi (DE-627)SPR041735781 (SPR)s13748-020-00216-0-e DE-627 ger DE-627 rakwb eng 004 600 ASE Mankad, Sapan H. verfasserin aut On the performance of empirical mode decomposition-based replay spoofing detection in speaker verification systems 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Automatic speaker verification (ASV) systems have maximum threat from replay spoofing attacks. High frequency regions of the underlying audio signal exhibit the phenomenon about their presence. It is therefore useful to decompose the underlying audio signal into frequency bands or regions for possible analysis. In this paper, an empirical mode decomposition (EMD)-based replay spoofing detection system is presented. Using EMD, each signal is decomposed into several monotonic intrinsic mode functions (IMFs). The signal is reconstructed and represented using one or more subsets of these IMFs by performing different combinations for spoofing detection. Results on ASVspoof 2017 version 2.0 and AVspoof benchmark replay attack datasets indicate that there is a potential in initial IMFs to carry replay attack patterns, and that is sufficient rather than processing the entire signal. The proposed approach can also serve as a preprocessing technique by employing dimension reduction strategy. Cross-corpus experiments on the systems indicate the limitations of ASV antispoofing systems due to mismatched conditions. Automatic speaker verification (dpeaa)DE-He213 Replay spoofing (dpeaa)DE-He213 Antispoofing (dpeaa)DE-He213 Empirical mode decomposition (dpeaa)DE-He213 Countermeasures (dpeaa)DE-He213 Garg, Sanjay verfasserin aut Enthalten in Progress in artificial intelligence Berlin : Springer, 2012 9(2020), 4 vom: 29. Aug., Seite 325-339 (DE-627)718730933 (DE-600)2668413-5 2192-6360 nnns volume:9 year:2020 number:4 day:29 month:08 pages:325-339 https://dx.doi.org/10.1007/s13748-020-00216-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2020 4 29 08 325-339
allfieldsSound	10.1007/s13748-020-00216-0 doi (DE-627)SPR041735781 (SPR)s13748-020-00216-0-e DE-627 ger DE-627 rakwb eng 004 600 ASE Mankad, Sapan H. verfasserin aut On the performance of empirical mode decomposition-based replay spoofing detection in speaker verification systems 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Automatic speaker verification (ASV) systems have maximum threat from replay spoofing attacks. High frequency regions of the underlying audio signal exhibit the phenomenon about their presence. It is therefore useful to decompose the underlying audio signal into frequency bands or regions for possible analysis. In this paper, an empirical mode decomposition (EMD)-based replay spoofing detection system is presented. Using EMD, each signal is decomposed into several monotonic intrinsic mode functions (IMFs). The signal is reconstructed and represented using one or more subsets of these IMFs by performing different combinations for spoofing detection. Results on ASVspoof 2017 version 2.0 and AVspoof benchmark replay attack datasets indicate that there is a potential in initial IMFs to carry replay attack patterns, and that is sufficient rather than processing the entire signal. The proposed approach can also serve as a preprocessing technique by employing dimension reduction strategy. Cross-corpus experiments on the systems indicate the limitations of ASV antispoofing systems due to mismatched conditions. Automatic speaker verification (dpeaa)DE-He213 Replay spoofing (dpeaa)DE-He213 Antispoofing (dpeaa)DE-He213 Empirical mode decomposition (dpeaa)DE-He213 Countermeasures (dpeaa)DE-He213 Garg, Sanjay verfasserin aut Enthalten in Progress in artificial intelligence Berlin : Springer, 2012 9(2020), 4 vom: 29. Aug., Seite 325-339 (DE-627)718730933 (DE-600)2668413-5 2192-6360 nnns volume:9 year:2020 number:4 day:29 month:08 pages:325-339 https://dx.doi.org/10.1007/s13748-020-00216-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2020 4 29 08 325-339
language	English
source	Enthalten in Progress in artificial intelligence 9(2020), 4 vom: 29. Aug., Seite 325-339 volume:9 year:2020 number:4 day:29 month:08 pages:325-339
sourceStr	Enthalten in Progress in artificial intelligence 9(2020), 4 vom: 29. Aug., Seite 325-339 volume:9 year:2020 number:4 day:29 month:08 pages:325-339
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Automatic speaker verification Replay spoofing Antispoofing Empirical mode decomposition Countermeasures
dewey-raw	004
isfreeaccess_bool	false
container_title	Progress in artificial intelligence
authorswithroles_txt_mv	Mankad, Sapan H. @@aut@@ Garg, Sanjay @@aut@@
publishDateDaySort_date	2020-08-29T00:00:00Z
hierarchy_top_id	718730933
dewey-sort	14
id	SPR041735781
language_de	englisch
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author	Mankad, Sapan H.
spellingShingle	Mankad, Sapan H. ddc 004 misc Automatic speaker verification misc Replay spoofing misc Antispoofing misc Empirical mode decomposition misc Countermeasures On the performance of empirical mode decomposition-based replay spoofing detection in speaker verification systems
authorStr	Mankad, Sapan H.
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topic_title	004 600 ASE On the performance of empirical mode decomposition-based replay spoofing detection in speaker verification systems Automatic speaker verification (dpeaa)DE-He213 Replay spoofing (dpeaa)DE-He213 Antispoofing (dpeaa)DE-He213 Empirical mode decomposition (dpeaa)DE-He213 Countermeasures (dpeaa)DE-He213
topic	ddc 004 misc Automatic speaker verification misc Replay spoofing misc Antispoofing misc Empirical mode decomposition misc Countermeasures
topic_unstemmed	ddc 004 misc Automatic speaker verification misc Replay spoofing misc Antispoofing misc Empirical mode decomposition misc Countermeasures
topic_browse	ddc 004 misc Automatic speaker verification misc Replay spoofing misc Antispoofing misc Empirical mode decomposition misc Countermeasures
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title	On the performance of empirical mode decomposition-based replay spoofing detection in speaker verification systems
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title_full	On the performance of empirical mode decomposition-based replay spoofing detection in speaker verification systems
author_sort	Mankad, Sapan H.
journal	Progress in artificial intelligence
journalStr	Progress in artificial intelligence
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author_browse	Mankad, Sapan H. Garg, Sanjay
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author-letter	Mankad, Sapan H.
doi_str_mv	10.1007/s13748-020-00216-0
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author2-role	verfasserin
title_sort	on the performance of empirical mode decomposition-based replay spoofing detection in speaker verification systems
title_auth	On the performance of empirical mode decomposition-based replay spoofing detection in speaker verification systems
abstract	Abstract Automatic speaker verification (ASV) systems have maximum threat from replay spoofing attacks. High frequency regions of the underlying audio signal exhibit the phenomenon about their presence. It is therefore useful to decompose the underlying audio signal into frequency bands or regions for possible analysis. In this paper, an empirical mode decomposition (EMD)-based replay spoofing detection system is presented. Using EMD, each signal is decomposed into several monotonic intrinsic mode functions (IMFs). The signal is reconstructed and represented using one or more subsets of these IMFs by performing different combinations for spoofing detection. Results on ASVspoof 2017 version 2.0 and AVspoof benchmark replay attack datasets indicate that there is a potential in initial IMFs to carry replay attack patterns, and that is sufficient rather than processing the entire signal. The proposed approach can also serve as a preprocessing technique by employing dimension reduction strategy. Cross-corpus experiments on the systems indicate the limitations of ASV antispoofing systems due to mismatched conditions.
abstractGer	Abstract Automatic speaker verification (ASV) systems have maximum threat from replay spoofing attacks. High frequency regions of the underlying audio signal exhibit the phenomenon about their presence. It is therefore useful to decompose the underlying audio signal into frequency bands or regions for possible analysis. In this paper, an empirical mode decomposition (EMD)-based replay spoofing detection system is presented. Using EMD, each signal is decomposed into several monotonic intrinsic mode functions (IMFs). The signal is reconstructed and represented using one or more subsets of these IMFs by performing different combinations for spoofing detection. Results on ASVspoof 2017 version 2.0 and AVspoof benchmark replay attack datasets indicate that there is a potential in initial IMFs to carry replay attack patterns, and that is sufficient rather than processing the entire signal. The proposed approach can also serve as a preprocessing technique by employing dimension reduction strategy. Cross-corpus experiments on the systems indicate the limitations of ASV antispoofing systems due to mismatched conditions.
abstract_unstemmed	Abstract Automatic speaker verification (ASV) systems have maximum threat from replay spoofing attacks. High frequency regions of the underlying audio signal exhibit the phenomenon about their presence. It is therefore useful to decompose the underlying audio signal into frequency bands or regions for possible analysis. In this paper, an empirical mode decomposition (EMD)-based replay spoofing detection system is presented. Using EMD, each signal is decomposed into several monotonic intrinsic mode functions (IMFs). The signal is reconstructed and represented using one or more subsets of these IMFs by performing different combinations for spoofing detection. Results on ASVspoof 2017 version 2.0 and AVspoof benchmark replay attack datasets indicate that there is a potential in initial IMFs to carry replay attack patterns, and that is sufficient rather than processing the entire signal. The proposed approach can also serve as a preprocessing technique by employing dimension reduction strategy. Cross-corpus experiments on the systems indicate the limitations of ASV antispoofing systems due to mismatched conditions.
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title_short	On the performance of empirical mode decomposition-based replay spoofing detection in speaker verification systems
url	https://dx.doi.org/10.1007/s13748-020-00216-0
remote_bool	true
author2	Garg, Sanjay
author2Str	Garg, Sanjay
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doi_str	10.1007/s13748-020-00216-0
up_date	2024-07-03T23:26:25.550Z
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High frequency regions of the underlying audio signal exhibit the phenomenon about their presence. It is therefore useful to decompose the underlying audio signal into frequency bands or regions for possible analysis. In this paper, an empirical mode decomposition (EMD)-based replay spoofing detection system is presented. Using EMD, each signal is decomposed into several monotonic intrinsic mode functions (IMFs). The signal is reconstructed and represented using one or more subsets of these IMFs by performing different combinations for spoofing detection. Results on ASVspoof 2017 version 2.0 and AVspoof benchmark replay attack datasets indicate that there is a potential in initial IMFs to carry replay attack patterns, and that is sufficient rather than processing the entire signal. The proposed approach can also serve as a preprocessing technique by employing dimension reduction strategy. 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