This book introduces methods for copyright protection and compression for speech signals. The first method introduces copyright protection of speech signal using watermarking; the second introduces compression of the speech signal using Compressive Sensing (CS). Both methods are tested and analyzed. The speech watermarking method uses technology such as Finite Ridgelet Transform (FRT), Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). The performance of the method is evaluated and compared with existing watermarking methods. In the speech compression method, the standard Compressive Sensing (CS) process is used for compression of the speech signal. The performance of the proposed method is evaluated using various transform bases like Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), and Fast Discrete Curvelet Transform (FDCuT).
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1 | (14) |
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1 | (1) |
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1.1.1 Properties and Characteristics of the Speech Signal |
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4 | (1) |
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1.2.1 Types of Watermarking |
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1.2.2 Requirements of Speech Watermarking |
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1.2.3 Applications of Watermarking |
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1.3.1 CS Acquisition Process |
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1.3.2 CS Reconstruction Process |
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1.3.3 Properties of CS Theory |
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1.4 Motivation for This Book |
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2.1 Signal Transformation |
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2.1.1 Discrete Fourier Transform |
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2.1.2 Discrete Cosine Transform |
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2.1.3 Discrete Wavelet Transform |
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2.1.4 Singular Value Decomposition |
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2.1.5 Fast Discrete Curvelet Transform |
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2.1.6 Finite Ridgelet Transform |
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2.1.7 Comparison of Signal Transformation |
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2.2 Arnold Scrambling Transform |
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2.3 Compressive Sensing Reconstruction Algorithms |
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2.3.1 Orthogonal Matching Pursuit |
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2.3.2 Compressive Sensing Matching Pursuit |
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3 Speech Watermarking Technique Using the Finite Ridgelet Transform, Discrete Wavelet Transform, and Singular Value Decomposition |
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3.1 Brief Overview of Watermarking Techniques for Digital Signals |
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3.2 Proposed Speech Watermarking Technique |
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3.2.1 Watermark Embedding Process |
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3.2.2 Watermark Extraction Process |
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3.3 Experimental Results and Discussion |
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3.3.1 Perceptual Transparency Test |
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3.3.5 Comparison of the Proposed Technique with Existing Techniques |
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3.4 Summary of Proposed Technique |
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4 Speech Compression Technique Using Compressive Sensing Theory |
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4.1 Brief Overview of Application of CS Theory to Digital Signals |
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4.2 Compression Technique Using CS Theory for Speech Signals |
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4.3 Experimental Results and Discussion |
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4.3.1 Analysis of a CS Theory-Based Compression Technique Using DFT |
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4.3.2 Analysis of a CS Theory-Based Compression Technique Using DCT |
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4.3.3 Analysis of a CS Theory-Based Compression Technique Using DWT |
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4.3.4 Analysis of a CS Theory-Based Compression Technique Using SVD |
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4.3.5 Analysis of a CS Theory-Based Compression Technique Using FDCuT |
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4.3.6 Comparison of the Presented Approaches |
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4.4 Summary of the Presented Work |
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5.1 Summary of the Presented Work |
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| Bibliography |
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Rohit Thanki obtained his Ph.D. in Multibiometric System Security from C. U. Shah University, Wadhwan city, Gujarat, India in 2017. His area of research interest is Digital Watermarking, Biometrics System, Security, Compressive Sensing, Pattern Recognition and Image Processing. He has published 2 books, 3 book chapters and 23 research papers to his credit in refereed & indexed journals, and conference at international and national level. His international recognition includes his professional memberships & services in refereed organizations, programme committees and reviewer for journals published by IEEE, Elsevier, Taylor & Francis, Springer, IGI-Global etc.
Komal Borisagar is currently an Associate Professor in the Department of ECE, Atmiya Institute of Technology and Science, Rajkot, Gujarat, India. She earned her Doctorate in Speech Processing form JJTU, Rajasthan in 2012. Her area of research interest is Digital Watermarking, Medical Image Compression, Wireless Communication, Speech Processing, and Image Processing. She has published 1 book chapters and more than 40 research papers to her credit in refereed & indexed journals, and conferences at international and national levels.
Surekha Borra is currently a Professor in the Department of ECE, K. S. Institute of Technology, Bangalore, Karnataka, India. She earned her Doctorate in Image Processing from Jawaharlal Nehru Technological University, Hyderabad, India, in 2015. Her research interests are in the areas of Image and Video Analytics, Machine Learning, Biometrics and Remote Sensing. She has published 4 book chapters and 22 research papers to her credit in refereed & indexed journals, and conferences at international and national levels. Her international recognition includes her professional memberships & services in refereed organizations, programme committees, editorial & review boards, wherein she has been a guest editor for 2journals and reviewer for journals published by IEEE, IET, Elsevier, Taylor & Francis, Springer, IGI-Global etc.,. She has received Woman Achiever's Award from The Institution of Engineers (India), for her prominent research and innovative contribution (s)., Woman Educator & Scholar Award for her contributions to teaching and scholarly activities, Young Woman Achiever Award for her contribution in Copyright Protection of Images.
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