Table Of ContentSPRINGER BRIEFS IN ELECTRICAL AND
COMPUTER ENGINEERING SPEECH TECHNOLOGY
Rohit Thanki
Komal Borisagar
Surekha Borra
Advance
Compression and
Watermarking
Technique for
Speech Signals
123
SpringerBriefs in Electrical and Computer
Engineering
Speech Technology
Series editor
Amy Neustein, Fort Lee, NJ, USA
Editor’s Note
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More information about this series at http://www.springer.com/series/10043
Rohit Thanki • Komal Borisagar • Surekha Borra
Advance Compression
and Watermarking
Technique for Speech Signals
Rohit Thanki Komal Borisagar
C. U. Shah University Electronics & Communication Engineering
Wadhwan City, Gujarat, India Department
Atmiya Institute of Technology and Science
Surekha Borra Rajkot, Gujarat, India
Electronics & Communication Engineering
Department
K.S. Institute of Technology
Bengaluru, Karnataka, India
ISSN 2191-8112 ISSN 2191-8120 (electronic)
SpringerBriefs in Electrical and Computer Engineering
ISSN 2191-737X ISSN 2191-7388 (electronic)
SpringerBriefs in Speech Technology
ISBN 978-3-319-69068-1 ISBN 978-3-319-69069-8 (eBook)
https://doi.org/10.1007/978-3-319-69069-8
Library of Congress Control Number: 2017956911
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Preface
With the invention of less expensive means of internet access, voice communication
via social media is on the rise, which often comprises threats and distortions.
Incorrect speaker/speech identification may sometimes lead to ambiguities in
speaker identification and misunderstandings. Therefore, proper identification of
speech is a must in speech communication. Speech watermarking is one solution,
when the owner of the speech is identified properly by embedding additional infor-
mation such as a watermark in the speech signal, in an imperceptible way. On the
other hand, voice communication also leads to a serious surge in speech storage
concerns.
This book introduces two techniques: copyright protection and compression of
speech signals. The first technique is aimed at providing security for speech signals
using watermarking with copyright protection techniques. The second technique is
aimed at providing compression for speech signals using compressive sensing (CS)
theory.
A speech watermarking technique using finite ridgelet transform (FRT), discrete
wavelet transform (DWT), and singular value decomposition (SVD) is presented. In
this technique, initially, the speech signal is taken as the host data and reshaped into
a matrix. FRT is applied to this matrix to obtain ridgelet coefficients of the host data.
Single-level DWT is performed on the ridgelet coefficients to obtain approximation
wavelet coefficients and detail wavelet coefficients. SVD is applied to the approxi-
mation wavelet coefficients and watermark data are then embedded into the singular
value by additive watermarking. Here, the Arnold transform is used for security of
the watermark data before embedding them into the host data. Watermark data are
extracted by using the singular value of the attacked watermarked speech signal and
the singular value of the original speech signal. The performance of the proposed
watermarking technique is evaluated and compared with existing watermarking
techniques. Simulation results have indicated that this watermarking outperforms
existing watermarking techniques in terms of robustness and perceptual
transparency.
A speech compression technique using CS theory is also presented in the book.
In this technique, initially the speech signal is taken and reshaped into a matrix. The
v
vi Preface
transform basis is applied to this matrix to obtain sparse coefficients of the signal.
The sparse measurement of the signal is then generated using the sparse coefficients
and the measurement matrix. The measurement matrix is a random matrix, which is
generated using different approaches such as random seed and Gaussian distribu-
tion. The measurement matrix is decided by the compression ratio of the speech
signal. The sparse measurement of data is fed to the CS recovery process along with
the proper measurement matrix, to get compressed sparse coefficients of the speech
signal. The inverse transform basis is applied to these compressed sparse coeffi-
cients to obtain compressed data in terms of a matrix. This matrix is reshaped into a
vector to get a compressed speech signal. The performance of this technique is
evaluated using various transform bases such as fast Fourier transform (FFT), dis-
crete cosine transform (DCT), DWT, SVD, and fast discrete curvelet transform
(FDCuT). Simulation results have indicated that this technique can be effectively
used for compression of the speech signal.
Keywords
Compression; Compressive sensing (CS); Copyright protection; Discrete cosine
transform (DCT); Discrete wavelet transform (DWT); Fast discrete curvelet trans-
form (FDCuT); Fast Fourier transform (FFT); Finite ridgelet transform (FRT);
Security; Singular value decomposition (SVD); Speech signal; Watermarking
vii
Contents
1 Introduction ............................................................................................... 1
1.1 Overview ............................................................................................ 1
1.1.1 Properties and Characteristics of the Speech Signal .............. 2
1.2 Digital Watermarking ......................................................................... 4
1.2.1 Types of Watermarking .......................................................... 5
1.2.2 Requirements of Speech Watermarking ................................. 6
1.2.3 Applications of Watermarking ............................................... 7
1.3 Compressive Sensing ......................................................................... 8
1.3.1 CS Acquisition Process .......................................................... 8
1.3.2 CS Reconstruction Process .................................................... 8
1.3.3 Properties of CS Theory ........................................................ 11
1.4 Motivation for This Book .................................................................. 12
1.5 Book Organization ............................................................................. 13
2 Background Information .......................................................................... 15
2.1 Signal Transformation ....................................................................... 15
2.1.1 Discrete Fourier Transform .................................................... 15
2.1.2 Discrete Cosine Transform .................................................... 16
2.1.3 Discrete Wavelet Transform ................................................... 17
2.1.4 Singular Value Decomposition .............................................. 19
2.1.5 Fast Discrete Curvelet Transform .......................................... 20
2.1.6 Finite Ridgelet Transform ...................................................... 21
2.1.7 Comparison of Signal Transformation ................................... 22
2.2 Arnold Scrambling Transform ........................................................... 22
2.3 Compressive Sensing Reconstruction Algorithms ............................. 24
2.3.1 Orthogonal Matching Pursuit ................................................ 24
2.3.2 Compressive Sensing Matching Pursuit ................................ 25
ix
x Contents
3 S peech Watermarking Technique Using the Finite
Ridgelet Transform, Discrete Wavelet Transform,
and Singular Value Decomposition .......................................................... 27
3.1 B rief Overview of Watermarking Techniques
for Digital Signals .............................................................................. 27
3.2 P roposed Speech Watermarking Technique ....................................... 30
3.2.1 Watermark Embedding Process ............................................. 30
3.2.2 Watermark Extraction Process ............................................... 32
3.3 E xperimental Results and Discussion ................................................ 33
3.3.1 Perceptual Transparency Test ................................................ 35
3.3.2 Robustness Test ...................................................................... 38
3.3.3 Error Analysis ........................................................................ 39
3.3.4 Embedding Capacity .............................................................. 41
3.3.5 Comparison of the Proposed Technique
with Existing Techniques ....................................................... 42
3.4 S ummary of Proposed Technique ...................................................... 45
4 Speech Compression Technique Using Compressive
Sensing Theory .......................................................................................... 47
4.1 Brief Overview of Application of CS Theory
to Digital Signals ............................................................................... 47
4.2 C ompression Technique Using CS Theory
for Speech Signals ............................................................................. 49
4.3 E xperimental Results and Discussion ................................................ 50
4.3.1 Analysis of a CS Theory–Based Compression
Technique Using DFT ............................................................ 51
4.3.2 Analysis of a CS Theory–Based Compression
Technique Using DCT ........................................................... 54
4.3.3 Analysis of a CS Theory–Based Compression
Technique Using DWT .......................................................... 56
4.3.4 Analysis of a CS Theory–Based Compression
Technique Using SVD ........................................................... 56
4.3.5 Analysis of a CS Theory–Based Compression
Technique Using FDCuT ....................................................... 59
4.3.6 Comparison of the Presented Approaches ............................. 61
4.4 S ummary of the Presented Work ....................................................... 63
5 C onclusions ................................................................................................ 65
5.1 S ummary of the Presented Work ....................................................... 65
5.2 F uture Research ................................................................................. 66
Bibliography .................................................................................................... 67
