Digital Speech Watermarking Based on Linear Predictive Analysis and Singular Value Decomposition
Abstract In this paper different digital audio watermarking techniques have been proposed. Currently, more attention is given to combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) techniques for watermarking purpose. Available DWT–SVD audio watermarking techniques...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Nematollahi, Mohammad Ali [verfasserIn] Vorakulpipat, Chalee [verfasserIn] Gamboa-Rosales, Hamurabi [verfasserIn] Martinez-Ruiz, Francisco J. [verfasserIn] De la Rosa-Vargas, Jose I. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Proceedings of the National Academy of Sciences - New York, NY : Springer, 2012, 87(2017), 3 vom: 26. Mai, Seite 433-446 |
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| Übergeordnetes Werk: |
volume:87 ; year:2017 ; number:3 ; day:26 ; month:05 ; pages:433-446 |
| Links: |
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| DOI / URN: |
10.1007/s40010-017-0371-8 |
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| Katalog-ID: |
SPR032623445 |
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| 100 | 1 | |a Nematollahi, Mohammad Ali |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Digital Speech Watermarking Based on Linear Predictive Analysis and Singular Value Decomposition |
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| 520 | |a Abstract In this paper different digital audio watermarking techniques have been proposed. Currently, more attention is given to combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) techniques for watermarking purpose. Available DWT–SVD audio watermarking techniques cannot be applied to speech signals efficiently. However, Linear Predictive Analysis (LPA) technique can model digital speech signals (20–30 ms) in more flexible and efficient ways than DWT. In this paper, a novel digital speech watermarking technique is proposed by applying both LPA and SVD. Quantization Index Modulation (QIM) is further applied to embed the watermark bits. The experimental results show that not only time and memory were reduced significantly as compared to different DWT–SVD audio watermarking techniques, but also the proposed technique was more robust and imperceptible for speech watermarking than other DWT–SVD audio watermarking techniques. | ||
| 650 | 4 | |a Digital speech watermarking |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Digital audio watermarking |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Linear predictive analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Singular value decomposition |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Quantization index modulation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Vorakulpipat, Chalee |e verfasserin |4 aut | |
| 700 | 1 | |a Gamboa-Rosales, Hamurabi |e verfasserin |4 aut | |
| 700 | 1 | |a Martinez-Ruiz, Francisco J. |e verfasserin |4 aut | |
| 700 | 1 | |a De la Rosa-Vargas, Jose I. |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Proceedings of the National Academy of Sciences |d New York, NY : Springer, 2012 |g 87(2017), 3 vom: 26. Mai, Seite 433-446 |w (DE-627)73921358X |w (DE-600)2707742-1 |x 2250-1762 |7 nnns |
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10.1007/s40010-017-0371-8 doi (DE-627)SPR032623445 (SPR)s40010-017-0371-8-e DE-627 ger DE-627 rakwb eng Nematollahi, Mohammad Ali verfasserin aut Digital Speech Watermarking Based on Linear Predictive Analysis and Singular Value Decomposition 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper different digital audio watermarking techniques have been proposed. Currently, more attention is given to combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) techniques for watermarking purpose. Available DWT–SVD audio watermarking techniques cannot be applied to speech signals efficiently. However, Linear Predictive Analysis (LPA) technique can model digital speech signals (20–30 ms) in more flexible and efficient ways than DWT. In this paper, a novel digital speech watermarking technique is proposed by applying both LPA and SVD. Quantization Index Modulation (QIM) is further applied to embed the watermark bits. The experimental results show that not only time and memory were reduced significantly as compared to different DWT–SVD audio watermarking techniques, but also the proposed technique was more robust and imperceptible for speech watermarking than other DWT–SVD audio watermarking techniques. Digital speech watermarking (dpeaa)DE-He213 Digital audio watermarking (dpeaa)DE-He213 Linear predictive analysis (dpeaa)DE-He213 Singular value decomposition (dpeaa)DE-He213 Quantization index modulation (dpeaa)DE-He213 Vorakulpipat, Chalee verfasserin aut Gamboa-Rosales, Hamurabi verfasserin aut Martinez-Ruiz, Francisco J. verfasserin aut De la Rosa-Vargas, Jose I. verfasserin aut Enthalten in Proceedings of the National Academy of Sciences New York, NY : Springer, 2012 87(2017), 3 vom: 26. Mai, Seite 433-446 (DE-627)73921358X (DE-600)2707742-1 2250-1762 nnns volume:87 year:2017 number:3 day:26 month:05 pages:433-446 https://dx.doi.org/10.1007/s40010-017-0371-8 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2017 3 26 05 433-446 |
| spelling |
10.1007/s40010-017-0371-8 doi (DE-627)SPR032623445 (SPR)s40010-017-0371-8-e DE-627 ger DE-627 rakwb eng Nematollahi, Mohammad Ali verfasserin aut Digital Speech Watermarking Based on Linear Predictive Analysis and Singular Value Decomposition 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper different digital audio watermarking techniques have been proposed. Currently, more attention is given to combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) techniques for watermarking purpose. Available DWT–SVD audio watermarking techniques cannot be applied to speech signals efficiently. However, Linear Predictive Analysis (LPA) technique can model digital speech signals (20–30 ms) in more flexible and efficient ways than DWT. In this paper, a novel digital speech watermarking technique is proposed by applying both LPA and SVD. Quantization Index Modulation (QIM) is further applied to embed the watermark bits. The experimental results show that not only time and memory were reduced significantly as compared to different DWT–SVD audio watermarking techniques, but also the proposed technique was more robust and imperceptible for speech watermarking than other DWT–SVD audio watermarking techniques. Digital speech watermarking (dpeaa)DE-He213 Digital audio watermarking (dpeaa)DE-He213 Linear predictive analysis (dpeaa)DE-He213 Singular value decomposition (dpeaa)DE-He213 Quantization index modulation (dpeaa)DE-He213 Vorakulpipat, Chalee verfasserin aut Gamboa-Rosales, Hamurabi verfasserin aut Martinez-Ruiz, Francisco J. verfasserin aut De la Rosa-Vargas, Jose I. verfasserin aut Enthalten in Proceedings of the National Academy of Sciences New York, NY : Springer, 2012 87(2017), 3 vom: 26. Mai, Seite 433-446 (DE-627)73921358X (DE-600)2707742-1 2250-1762 nnns volume:87 year:2017 number:3 day:26 month:05 pages:433-446 https://dx.doi.org/10.1007/s40010-017-0371-8 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2017 3 26 05 433-446 |
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10.1007/s40010-017-0371-8 doi (DE-627)SPR032623445 (SPR)s40010-017-0371-8-e DE-627 ger DE-627 rakwb eng Nematollahi, Mohammad Ali verfasserin aut Digital Speech Watermarking Based on Linear Predictive Analysis and Singular Value Decomposition 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper different digital audio watermarking techniques have been proposed. Currently, more attention is given to combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) techniques for watermarking purpose. Available DWT–SVD audio watermarking techniques cannot be applied to speech signals efficiently. However, Linear Predictive Analysis (LPA) technique can model digital speech signals (20–30 ms) in more flexible and efficient ways than DWT. In this paper, a novel digital speech watermarking technique is proposed by applying both LPA and SVD. Quantization Index Modulation (QIM) is further applied to embed the watermark bits. The experimental results show that not only time and memory were reduced significantly as compared to different DWT–SVD audio watermarking techniques, but also the proposed technique was more robust and imperceptible for speech watermarking than other DWT–SVD audio watermarking techniques. Digital speech watermarking (dpeaa)DE-He213 Digital audio watermarking (dpeaa)DE-He213 Linear predictive analysis (dpeaa)DE-He213 Singular value decomposition (dpeaa)DE-He213 Quantization index modulation (dpeaa)DE-He213 Vorakulpipat, Chalee verfasserin aut Gamboa-Rosales, Hamurabi verfasserin aut Martinez-Ruiz, Francisco J. verfasserin aut De la Rosa-Vargas, Jose I. verfasserin aut Enthalten in Proceedings of the National Academy of Sciences New York, NY : Springer, 2012 87(2017), 3 vom: 26. Mai, Seite 433-446 (DE-627)73921358X (DE-600)2707742-1 2250-1762 nnns volume:87 year:2017 number:3 day:26 month:05 pages:433-446 https://dx.doi.org/10.1007/s40010-017-0371-8 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2017 3 26 05 433-446 |
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10.1007/s40010-017-0371-8 doi (DE-627)SPR032623445 (SPR)s40010-017-0371-8-e DE-627 ger DE-627 rakwb eng Nematollahi, Mohammad Ali verfasserin aut Digital Speech Watermarking Based on Linear Predictive Analysis and Singular Value Decomposition 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper different digital audio watermarking techniques have been proposed. Currently, more attention is given to combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) techniques for watermarking purpose. Available DWT–SVD audio watermarking techniques cannot be applied to speech signals efficiently. However, Linear Predictive Analysis (LPA) technique can model digital speech signals (20–30 ms) in more flexible and efficient ways than DWT. In this paper, a novel digital speech watermarking technique is proposed by applying both LPA and SVD. Quantization Index Modulation (QIM) is further applied to embed the watermark bits. The experimental results show that not only time and memory were reduced significantly as compared to different DWT–SVD audio watermarking techniques, but also the proposed technique was more robust and imperceptible for speech watermarking than other DWT–SVD audio watermarking techniques. Digital speech watermarking (dpeaa)DE-He213 Digital audio watermarking (dpeaa)DE-He213 Linear predictive analysis (dpeaa)DE-He213 Singular value decomposition (dpeaa)DE-He213 Quantization index modulation (dpeaa)DE-He213 Vorakulpipat, Chalee verfasserin aut Gamboa-Rosales, Hamurabi verfasserin aut Martinez-Ruiz, Francisco J. verfasserin aut De la Rosa-Vargas, Jose I. verfasserin aut Enthalten in Proceedings of the National Academy of Sciences New York, NY : Springer, 2012 87(2017), 3 vom: 26. Mai, Seite 433-446 (DE-627)73921358X (DE-600)2707742-1 2250-1762 nnns volume:87 year:2017 number:3 day:26 month:05 pages:433-446 https://dx.doi.org/10.1007/s40010-017-0371-8 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2017 3 26 05 433-446 |
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10.1007/s40010-017-0371-8 doi (DE-627)SPR032623445 (SPR)s40010-017-0371-8-e DE-627 ger DE-627 rakwb eng Nematollahi, Mohammad Ali verfasserin aut Digital Speech Watermarking Based on Linear Predictive Analysis and Singular Value Decomposition 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper different digital audio watermarking techniques have been proposed. Currently, more attention is given to combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) techniques for watermarking purpose. Available DWT–SVD audio watermarking techniques cannot be applied to speech signals efficiently. However, Linear Predictive Analysis (LPA) technique can model digital speech signals (20–30 ms) in more flexible and efficient ways than DWT. In this paper, a novel digital speech watermarking technique is proposed by applying both LPA and SVD. Quantization Index Modulation (QIM) is further applied to embed the watermark bits. The experimental results show that not only time and memory were reduced significantly as compared to different DWT–SVD audio watermarking techniques, but also the proposed technique was more robust and imperceptible for speech watermarking than other DWT–SVD audio watermarking techniques. Digital speech watermarking (dpeaa)DE-He213 Digital audio watermarking (dpeaa)DE-He213 Linear predictive analysis (dpeaa)DE-He213 Singular value decomposition (dpeaa)DE-He213 Quantization index modulation (dpeaa)DE-He213 Vorakulpipat, Chalee verfasserin aut Gamboa-Rosales, Hamurabi verfasserin aut Martinez-Ruiz, Francisco J. verfasserin aut De la Rosa-Vargas, Jose I. verfasserin aut Enthalten in Proceedings of the National Academy of Sciences New York, NY : Springer, 2012 87(2017), 3 vom: 26. Mai, Seite 433-446 (DE-627)73921358X (DE-600)2707742-1 2250-1762 nnns volume:87 year:2017 number:3 day:26 month:05 pages:433-446 https://dx.doi.org/10.1007/s40010-017-0371-8 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2017 3 26 05 433-446 |
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Enthalten in Proceedings of the National Academy of Sciences 87(2017), 3 vom: 26. Mai, Seite 433-446 volume:87 year:2017 number:3 day:26 month:05 pages:433-446 |
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Nematollahi, Mohammad Ali @@aut@@ Vorakulpipat, Chalee @@aut@@ Gamboa-Rosales, Hamurabi @@aut@@ Martinez-Ruiz, Francisco J. @@aut@@ De la Rosa-Vargas, Jose I. @@aut@@ |
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Nematollahi, Mohammad Ali |
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Nematollahi, Mohammad Ali misc Digital speech watermarking misc Digital audio watermarking misc Linear predictive analysis misc Singular value decomposition misc Quantization index modulation Digital Speech Watermarking Based on Linear Predictive Analysis and Singular Value Decomposition |
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Digital Speech Watermarking Based on Linear Predictive Analysis and Singular Value Decomposition Digital speech watermarking (dpeaa)DE-He213 Digital audio watermarking (dpeaa)DE-He213 Linear predictive analysis (dpeaa)DE-He213 Singular value decomposition (dpeaa)DE-He213 Quantization index modulation (dpeaa)DE-He213 |
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misc Digital speech watermarking misc Digital audio watermarking misc Linear predictive analysis misc Singular value decomposition misc Quantization index modulation |
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misc Digital speech watermarking misc Digital audio watermarking misc Linear predictive analysis misc Singular value decomposition misc Quantization index modulation |
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Digital Speech Watermarking Based on Linear Predictive Analysis and Singular Value Decomposition |
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Digital Speech Watermarking Based on Linear Predictive Analysis and Singular Value Decomposition |
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Nematollahi, Mohammad Ali Vorakulpipat, Chalee Gamboa-Rosales, Hamurabi Martinez-Ruiz, Francisco J. De la Rosa-Vargas, Jose I. |
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digital speech watermarking based on linear predictive analysis and singular value decomposition |
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Digital Speech Watermarking Based on Linear Predictive Analysis and Singular Value Decomposition |
| abstract |
Abstract In this paper different digital audio watermarking techniques have been proposed. Currently, more attention is given to combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) techniques for watermarking purpose. Available DWT–SVD audio watermarking techniques cannot be applied to speech signals efficiently. However, Linear Predictive Analysis (LPA) technique can model digital speech signals (20–30 ms) in more flexible and efficient ways than DWT. In this paper, a novel digital speech watermarking technique is proposed by applying both LPA and SVD. Quantization Index Modulation (QIM) is further applied to embed the watermark bits. The experimental results show that not only time and memory were reduced significantly as compared to different DWT–SVD audio watermarking techniques, but also the proposed technique was more robust and imperceptible for speech watermarking than other DWT–SVD audio watermarking techniques. |
| abstractGer |
Abstract In this paper different digital audio watermarking techniques have been proposed. Currently, more attention is given to combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) techniques for watermarking purpose. Available DWT–SVD audio watermarking techniques cannot be applied to speech signals efficiently. However, Linear Predictive Analysis (LPA) technique can model digital speech signals (20–30 ms) in more flexible and efficient ways than DWT. In this paper, a novel digital speech watermarking technique is proposed by applying both LPA and SVD. Quantization Index Modulation (QIM) is further applied to embed the watermark bits. The experimental results show that not only time and memory were reduced significantly as compared to different DWT–SVD audio watermarking techniques, but also the proposed technique was more robust and imperceptible for speech watermarking than other DWT–SVD audio watermarking techniques. |
| abstract_unstemmed |
Abstract In this paper different digital audio watermarking techniques have been proposed. Currently, more attention is given to combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) techniques for watermarking purpose. Available DWT–SVD audio watermarking techniques cannot be applied to speech signals efficiently. However, Linear Predictive Analysis (LPA) technique can model digital speech signals (20–30 ms) in more flexible and efficient ways than DWT. In this paper, a novel digital speech watermarking technique is proposed by applying both LPA and SVD. Quantization Index Modulation (QIM) is further applied to embed the watermark bits. The experimental results show that not only time and memory were reduced significantly as compared to different DWT–SVD audio watermarking techniques, but also the proposed technique was more robust and imperceptible for speech watermarking than other DWT–SVD audio watermarking techniques. |
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Digital Speech Watermarking Based on Linear Predictive Analysis and Singular Value Decomposition |
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https://dx.doi.org/10.1007/s40010-017-0371-8 |
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Vorakulpipat, Chalee Gamboa-Rosales, Hamurabi Martinez-Ruiz, Francisco J. De la Rosa-Vargas, Jose I. |
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Vorakulpipat, Chalee Gamboa-Rosales, Hamurabi Martinez-Ruiz, Francisco J. De la Rosa-Vargas, Jose I. |
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| doi_str |
10.1007/s40010-017-0371-8 |
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2024-07-03T13:53:54.587Z |
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