Advance Compression and Watermarking Technique for Speech Signals

82 Pages·2018·4.899 MB·English

by  Rohit Thanki, Komal Borisagar, Surekha Borra

SPRINGER 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
The authors of this series have been hand-selected. They comprise some of the most
outstanding scientists—drawn from academia and private industry—whose research
is marked by its novelty, applicability, and practicality in providing broad based
speech solutions. The SpringerBriefs in Speech Technology series provides the
latest findings in speech technology gleaned from comprehensive literature reviews
and empirical investigations that are performed in both laboratory and real life
settings. Some of the topics covered in this series include the presentation of real
life commercial deployment of spoken dialog systems, contemporary methods of
speech parameterization, developments in information security for automated
speech, forensic speaker recognition, use of sophisticated speech analytics in call
centers, and an exploration of new methods of soft computing for improving human-
computer interaction. Those in academia, the private sector, the self service industry,
law enforcement, and government intelligence, are among the principal audience
for this series, which is designed to serve as an important and essential reference
guide for speech developers, system designers, speech engineers, linguists and
others. In particular, a major audience of readers will consist of researchers and
technical experts in the automated call center industry where speech processing is a
key component to the functioning of customer care contact centers.
Amy Neustein, Ph.D., serves as Editor-in-Chief of the International Journal of
Speech Technology (Springer). She edited the recently published book “Advances in
Speech Recognition: Mobile Environments, Call Centers and Clinics” (Springer
2010), and serves as quest columnist on speech processing for Womensenews.
Dr. Neustein is Founder and CEO of Linguistic Technology Systems, a NJ-based
think tank for intelligent design of advanced natural language based emotion-
detection software to improve human response in monitoring recorded conversations
of terror suspects and helpline calls. Dr. Neustein’s work appears in the peer review
literature and in industry and mass media publications. Her academic books, which
cover a range of political, social and legal topics, have been cited in the Chronicles
of Higher Education, and have won her a pro Humanitate Literary Award. She
serves on the visiting faculty of the National Judicial College and as a plenary
speaker at conferences in artificial intelligence and computing. Dr. Neustein is a
member of MIR (machine intelligence research) Labs, which does advanced work in
computer technology to assist underdeveloped countries in improving their ability
to cope with famine, disease/illness, and political and social affliction. She is a
founding member of the New York City Speech Processing Consortium, a newly
formed group of NY-based companies, publishing houses, and researchers dedicated
to advancing speech technology research and development.
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
© The Author(s) 2018
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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