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Audio Analysis and Synthesis

DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS


Deep neural networks (DNNs) have been successfully deployed for acoustic modelling in statistical parametric speech synthesis (SPSS) systems. Moreover, DNN-based postfilters (PF) have also been shown to outperform conventional postfilters that are widely used in SPSS systems for increasing the quality of synthesized speech. However, existing DNN-based postfilters are trained with speaker-dependent databases. Given that SPSS systems can rapidly adapt to new speakers from generic models, there is a need for DNN-based postfilters that can adapt to new speakers with minimal adaptation data.

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Authors:
Miraç Göksu Öztürk, Okan Ulusoy, Cenk Demiroglu
Submitted On:
10 May 2019 - 7:36am
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ICASSP_2019_v1.pptx

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[1] Miraç Göksu Öztürk, Okan Ulusoy, Cenk Demiroglu, "DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4306. Accessed: Jun. 26, 2019.
@article{4306-19,
url = {http://sigport.org/4306},
author = {Miraç Göksu Öztürk; Okan Ulusoy; Cenk Demiroglu },
publisher = {IEEE SigPort},
title = {DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS},
year = {2019} }
TY - EJOUR
T1 - DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS
AU - Miraç Göksu Öztürk; Okan Ulusoy; Cenk Demiroglu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4306
ER -
Miraç Göksu Öztürk, Okan Ulusoy, Cenk Demiroglu. (2019). DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS. IEEE SigPort. http://sigport.org/4306
Miraç Göksu Öztürk, Okan Ulusoy, Cenk Demiroglu, 2019. DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS. Available at: http://sigport.org/4306.
Miraç Göksu Öztürk, Okan Ulusoy, Cenk Demiroglu. (2019). "DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS." Web.
1. Miraç Göksu Öztürk, Okan Ulusoy, Cenk Demiroglu. DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4306

F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT


Pitch plays a significant role in understanding a tone based language like Mandarin. In this paper, we present a new method that estimates F0 contour for electrolaryngeal (EL) speech enhancement in Mandarin. Our system explores the usage of phonetic feature to improve the quality of EL speech. First, we train an acoustic model for EL speech and generate the phoneme posterior probabilities feature sequence for each input EL speech utterance. Then we employ the phonetic feature for F0 contour generation rather than the acoustic feature.

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Authors:
Zexin Cai, Zhicheng Xu, Ming Li
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7 May 2019 - 11:25pm
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ICASSP.2019.8683435-poster

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[1] Zexin Cai, Zhicheng Xu, Ming Li, "F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4000. Accessed: Jun. 26, 2019.
@article{4000-19,
url = {http://sigport.org/4000},
author = {Zexin Cai; Zhicheng Xu; Ming Li },
publisher = {IEEE SigPort},
title = {F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT},
year = {2019} }
TY - EJOUR
T1 - F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT
AU - Zexin Cai; Zhicheng Xu; Ming Li
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4000
ER -
Zexin Cai, Zhicheng Xu, Ming Li. (2019). F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT. IEEE SigPort. http://sigport.org/4000
Zexin Cai, Zhicheng Xu, Ming Li, 2019. F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT. Available at: http://sigport.org/4000.
Zexin Cai, Zhicheng Xu, Ming Li. (2019). "F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT." Web.
1. Zexin Cai, Zhicheng Xu, Ming Li. F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4000

Tutorial T-9: Model-based Speech and Audio Processing

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Authors:
Mads Græsbøll Christensen, Jesper Kjær Nielsen, and Jesper Rindom Jensen
Submitted On:
16 April 2018 - 5:52pm
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[1] Mads Græsbøll Christensen, Jesper Kjær Nielsen, and Jesper Rindom Jensen, "Tutorial T-9: Model-based Speech and Audio Processing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2917. Accessed: Jun. 26, 2019.
@article{2917-18,
url = {http://sigport.org/2917},
author = {Mads Græsbøll Christensen; Jesper Kjær Nielsen; and Jesper Rindom Jensen },
publisher = {IEEE SigPort},
title = {Tutorial T-9: Model-based Speech and Audio Processing},
year = {2018} }
TY - EJOUR
T1 - Tutorial T-9: Model-based Speech and Audio Processing
AU - Mads Græsbøll Christensen; Jesper Kjær Nielsen; and Jesper Rindom Jensen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2917
ER -
Mads Græsbøll Christensen, Jesper Kjær Nielsen, and Jesper Rindom Jensen. (2018). Tutorial T-9: Model-based Speech and Audio Processing. IEEE SigPort. http://sigport.org/2917
Mads Græsbøll Christensen, Jesper Kjær Nielsen, and Jesper Rindom Jensen, 2018. Tutorial T-9: Model-based Speech and Audio Processing. Available at: http://sigport.org/2917.
Mads Græsbøll Christensen, Jesper Kjær Nielsen, and Jesper Rindom Jensen. (2018). "Tutorial T-9: Model-based Speech and Audio Processing." Web.
1. Mads Græsbøll Christensen, Jesper Kjær Nielsen, and Jesper Rindom Jensen. Tutorial T-9: Model-based Speech and Audio Processing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2917

Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations


Audio annotation is an important step in developing machine-listening systems. It is also a time consuming process, which has motivated investigators to crowdsource audio annotations. However, there are many factors that affect annotations, many of which have not been adequately investigated. In previous work, we investigated the effects of visualization aids and sound scene complexity on the quality of crowdsourced sound-event annotations.

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Authors:
Mark Cartwright, Justin Salamon, Ayanna Seals, Oded Nov, Juan Pablo Bello
Submitted On:
14 April 2018 - 5:17pm
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[1] Mark Cartwright, Justin Salamon, Ayanna Seals, Oded Nov, Juan Pablo Bello, "Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2853. Accessed: Jun. 26, 2019.
@article{2853-18,
url = {http://sigport.org/2853},
author = {Mark Cartwright; Justin Salamon; Ayanna Seals; Oded Nov; Juan Pablo Bello },
publisher = {IEEE SigPort},
title = {Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations},
year = {2018} }
TY - EJOUR
T1 - Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations
AU - Mark Cartwright; Justin Salamon; Ayanna Seals; Oded Nov; Juan Pablo Bello
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2853
ER -
Mark Cartwright, Justin Salamon, Ayanna Seals, Oded Nov, Juan Pablo Bello. (2018). Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations. IEEE SigPort. http://sigport.org/2853
Mark Cartwright, Justin Salamon, Ayanna Seals, Oded Nov, Juan Pablo Bello, 2018. Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations. Available at: http://sigport.org/2853.
Mark Cartwright, Justin Salamon, Ayanna Seals, Oded Nov, Juan Pablo Bello. (2018). "Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations." Web.
1. Mark Cartwright, Justin Salamon, Ayanna Seals, Oded Nov, Juan Pablo Bello. Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2853

SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS


This paper presents a SampleRNN-based neural vocoder for statistical parametric speech synthesis. This method utilizes a conditional SampleRNN model composed of a hierarchical structure of GRU layers and feed-forward layers to capture long-span dependencies between acoustic features and waveform sequences. Compared with conventional vocoders based on the source-filter model, our proposed vocoder is trained without assumptions derived from the prior knowledge of speech production and is able to provide a better modeling and recovery of phase information.

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Authors:
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling
Submitted On:
13 April 2018 - 3:29am
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ICASSP2018_poster_aiyang.pdf

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[1] Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling, "SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2633. Accessed: Jun. 26, 2019.
@article{2633-18,
url = {http://sigport.org/2633},
author = {Yang Ai; Hong-Chuan Wu; Zhen-Hua Ling },
publisher = {IEEE SigPort},
title = {SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS},
year = {2018} }
TY - EJOUR
T1 - SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS
AU - Yang Ai; Hong-Chuan Wu; Zhen-Hua Ling
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2633
ER -
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling. (2018). SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS. IEEE SigPort. http://sigport.org/2633
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling, 2018. SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS. Available at: http://sigport.org/2633.
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling. (2018). "SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS." Web.
1. Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling. SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2633

SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS


This paper presents a SampleRNN-based neural vocoder for statistical parametric speech synthesis. This method utilizes a conditional SampleRNN model composed of a hierarchical structure of GRU layers and feed-forward layers to capture long-span dependencies between acoustic features and waveform sequences. Compared with conventional vocoders based on the source-filter model, our proposed vocoder is trained without assumptions derived from the prior knowledge of speech production and is able to provide a better modeling and recovery of phase information.

Paper Details

Authors:
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling
Submitted On:
13 April 2018 - 3:29am
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[1] Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling, "SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2632. Accessed: Jun. 26, 2019.
@article{2632-18,
url = {http://sigport.org/2632},
author = {Yang Ai; Hong-Chuan Wu; Zhen-Hua Ling },
publisher = {IEEE SigPort},
title = {SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS},
year = {2018} }
TY - EJOUR
T1 - SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS
AU - Yang Ai; Hong-Chuan Wu; Zhen-Hua Ling
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2632
ER -
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling. (2018). SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS. IEEE SigPort. http://sigport.org/2632
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling, 2018. SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS. Available at: http://sigport.org/2632.
Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling. (2018). "SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS." Web.
1. Yang Ai, Hong-Chuan Wu, Zhen-Hua Ling. SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2632

REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS


Being able to predict whether a song can be a hit has important applications in the music industry. Although it is true that the popularity of a song can be greatly affected by external factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own.

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Authors:
Li-Chia Yang, Szu-Yu Chou, Jen-Yu Liu, Yi-Hsuan Yang, Yi-An Chen
Submitted On:
3 March 2017 - 12:59am
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[1] Li-Chia Yang, Szu-Yu Chou, Jen-Yu Liu, Yi-Hsuan Yang, Yi-An Chen, "REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1600. Accessed: Jun. 26, 2019.
@article{1600-17,
url = {http://sigport.org/1600},
author = {Li-Chia Yang; Szu-Yu Chou; Jen-Yu Liu; Yi-Hsuan Yang; Yi-An Chen },
publisher = {IEEE SigPort},
title = {REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS},
year = {2017} }
TY - EJOUR
T1 - REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS
AU - Li-Chia Yang; Szu-Yu Chou; Jen-Yu Liu; Yi-Hsuan Yang; Yi-An Chen
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1600
ER -
Li-Chia Yang, Szu-Yu Chou, Jen-Yu Liu, Yi-Hsuan Yang, Yi-An Chen. (2017). REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/1600
Li-Chia Yang, Szu-Yu Chou, Jen-Yu Liu, Yi-Hsuan Yang, Yi-An Chen, 2017. REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/1600.
Li-Chia Yang, Szu-Yu Chou, Jen-Yu Liu, Yi-Hsuan Yang, Yi-An Chen. (2017). "REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS." Web.
1. Li-Chia Yang, Szu-Yu Chou, Jen-Yu Liu, Yi-Hsuan Yang, Yi-An Chen. REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1600

Global Variance in Speech Synthesis with Linear Dynamical Models

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Authors:
Vassilis Tsiaras, Ranniery Maia, Vassilis Diakoloukas, Yannis Stylianou, Vassilis Digalakis
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11 March 2017 - 8:48pm
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[1] Vassilis Tsiaras, Ranniery Maia, Vassilis Diakoloukas, Yannis Stylianou, Vassilis Digalakis, "Global Variance in Speech Synthesis with Linear Dynamical Models", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1597. Accessed: Jun. 26, 2019.
@article{1597-17,
url = {http://sigport.org/1597},
author = {Vassilis Tsiaras; Ranniery Maia; Vassilis Diakoloukas; Yannis Stylianou; Vassilis Digalakis },
publisher = {IEEE SigPort},
title = {Global Variance in Speech Synthesis with Linear Dynamical Models},
year = {2017} }
TY - EJOUR
T1 - Global Variance in Speech Synthesis with Linear Dynamical Models
AU - Vassilis Tsiaras; Ranniery Maia; Vassilis Diakoloukas; Yannis Stylianou; Vassilis Digalakis
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1597
ER -
Vassilis Tsiaras, Ranniery Maia, Vassilis Diakoloukas, Yannis Stylianou, Vassilis Digalakis. (2017). Global Variance in Speech Synthesis with Linear Dynamical Models. IEEE SigPort. http://sigport.org/1597
Vassilis Tsiaras, Ranniery Maia, Vassilis Diakoloukas, Yannis Stylianou, Vassilis Digalakis, 2017. Global Variance in Speech Synthesis with Linear Dynamical Models. Available at: http://sigport.org/1597.
Vassilis Tsiaras, Ranniery Maia, Vassilis Diakoloukas, Yannis Stylianou, Vassilis Digalakis. (2017). "Global Variance in Speech Synthesis with Linear Dynamical Models." Web.
1. Vassilis Tsiaras, Ranniery Maia, Vassilis Diakoloukas, Yannis Stylianou, Vassilis Digalakis. Global Variance in Speech Synthesis with Linear Dynamical Models [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1597

poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK

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24 March 2016 - 10:48am
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[1] , "poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1026. Accessed: Jun. 26, 2019.
@article{1026-16,
url = {http://sigport.org/1026},
author = { },
publisher = {IEEE SigPort},
title = {poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK},
year = {2016} }
TY - EJOUR
T1 - poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1026
ER -
. (2016). poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK. IEEE SigPort. http://sigport.org/1026
, 2016. poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK. Available at: http://sigport.org/1026.
. (2016). "poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK." Web.
1. . poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1026

poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK

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[1] , "poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1025. Accessed: Jun. 26, 2019.
@article{1025-16,
url = {http://sigport.org/1025},
author = { },
publisher = {IEEE SigPort},
title = {poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK},
year = {2016} }
TY - EJOUR
T1 - poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1025
ER -
. (2016). poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK. IEEE SigPort. http://sigport.org/1025
, 2016. poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK. Available at: http://sigport.org/1025.
. (2016). "poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK." Web.
1. . poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1025

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