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Application of Wavelets in Speech Processing 2nd ed. 2018 [Pehme köide]

  • Formaat: Paperback / softback, 86 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 12 Illustrations, color; 13 Illustrations, black and white; XIV, 86 p. 25 illus., 12 illus. in color., 1 Paperback / softback
  • Sari: SpringerBriefs in Speech Technology
  • Ilmumisaeg: 07-Dec-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319690019
  • ISBN-13: 9783319690018
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Application of Wavelets in Speech Processing 2nd ed. 2018Suurem pilt
  • Formaat: Paperback / softback, 86 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 12 Illustrations, color; 13 Illustrations, black and white; XIV, 86 p. 25 illus., 12 illus. in color., 1 Paperback / softback
  • Sari: SpringerBriefs in Speech Technology
  • Ilmumisaeg: 07-Dec-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319690019
  • ISBN-13: 9783319690018
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319690018.html
This new edition provides an updated and enhanced survey on employing wavelets analysis in an array of applications of speech processing. The author presents updated developments in topics such as; speech enhancement, noise suppression, spectral analysis of speech signal, speech quality assessment, speech recognition, forensics by Speech, and emotion recognition from speech. The new edition also features  a new chapter on scalogram analysis of speech.





Moreover, in this edition, each chapter is restructured as such; that it becomes self contained, and can be read separately. Each chapter surveys the literature in a topic such that the use of wavelets in the work is explained and experimental results of proposed method are then discussed. Illustrative figures are also added to explain the methodology of each work.
1 Introduction
1(4)
1.1 History and Definition of Speech Processing
1(1)
1.2 Applications of Speech Processing
2(1)
1.3 Recent Progress in Speech Processing
2(1)
1.4 Wavelet Analysis as an Efficient Tool for Speech Processing
3(2)
References
4(1)
2 Speech Production and Perception
5(6)
2.1 Speech Production Process
5(1)
2.2 Classification of Speech Sounds
6(1)
2.3 Speech Production Modeling
7(1)
2.4 Speech Perception Modeling
8(1)
2.5 Intelligibility and Speech Quality Measures
9(2)
References
10(1)
3 Wavelets, Wavelet Filters, and Wavelet Transforms
11(12)
3.1 Short-Time Fourier Transform (STFT)
11(1)
3.2 Multiresolution Analysis and Wavelet Transform
12(2)
3.3 Wavelets and Bank of Filters
14(1)
3.4 Wavelet Families
15(1)
3.5 Wavelet Packets
16(2)
3.6 Undecimated Wavelet Transform
18(1)
3.7 The Continuous Wavelet Transform (CWT)
18(1)
3.8 Wavelet Scalogram
19(1)
3.9 Empirical Wavelets
19(4)
References
20(3)
4 Spectral Analysis of Speech Signal and Pitch Estimation
23(6)
4.1 Spectral Analysis
23(1)
4.2 Formant Tracking and Estimation
24(1)
4.3 Pitch Estimation
25(4)
References
27(2)
5 Speech Detection and Separation
29(6)
5.1 Voice Activity Detection
29(1)
5.2 Segmentation of Speech Signal
30(1)
5.3 Source Separation of Speech
31(4)
References
33(2)
6 Speech Enhancement and Noise Suppression
35(6)
6.1 Thresholding Schemes
36(1)
6.2 Thresholding on Wavelet Packet Coefficients
37(1)
6.3 Enhancement on Multitaper Spectrum
38(3)
References
39(2)
7 Speech Recognition
41(6)
7.1 Signal Enhancement and Noise Cancellation for Robust Recognition
41(1)
7.2 Wavelet-Based Features for Better Recognition
42(1)
7.3 Hybrid Approach
43(1)
7.4 Wavelet as an Activation Function for Neural Networks in ASR
44(3)
References
45(2)
8 Speaker Identification
47(4)
8.1 Wavelet-Based Features for Speaker Identification
48(1)
8.2 Hybrid Feature Sets for Speaker Identification
49(2)
References
49(2)
9 Emotion Recognition from Speech
51(6)
9.1 Wavelet-Based Features for Emotion Recognition
51(2)
9.2 Combined Feature Set for Better Emotion Recognition
53(1)
9.3 WNN for Emotion Recognition
54(3)
References
54(3)
10 Speech Coding, Synthesis, and Compression
57(4)
10.1 Speech Synthesis
57(1)
10.2 Speech Coding and Compression
58(1)
10.3 Real-Time Implementation of DWT-Based Speech Compression
58(3)
References
59(2)
11 Speech Quality Assessment
61(4)
11.1 Wavelet-Packet Analysis
61(2)
11.2 Discrete Wavelet Transform
63(2)
References
64(1)
12 Scalogram and Nonlinear Analysis of Speech
65(6)
12.1 Wavelet-Based Nonlinear Features
65(1)
12.2 Wavelet Scalogram Analysis
66(1)
12.3 Nonlinear and Chaotic Components in Speech Signal
67(4)
References
69(2)
13 Steganography, Forensics, and Security of Speech Signal
71(6)
13.1 Secure Communication of Speech
71(2)
13.2 Watermarking of Speech
73(1)
13.3 Watermarking in Sparse Representation
73(1)
13.4 Forensic Analysis of Speech
74(3)
References
75(2)
14 Clinical Diagnosis and Assessment of Speech Pathology
77(4)
References
79(2)
Index 81
Mohamed Hesham Farouk El-Sayed is a full professor with the Engineering Math & Physics Department within the Faculty of Engineering at Cairo University. He obtained his B.Sc. in electronics and telecommunications engineering with honors on 1982, another B.Sc. in physics on 1985 and M.Sc. in engineering physics on 1989 all from Cairo university. He received his Ph.D. in Engineering Physics from Cairo University on 1993. He is the author and coauthor of several published papers on the application of wavelets in the analysis of speech and on modeling of speech production in reputable periodicals and conferences since 1993. He is also the author of Application of Wavelets in Speech Processing (Springer 2014). M. Hesham has been actively involved in several national R&D projects on speech recognition since 1982.