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E-raamat: Speech Enhancement Techniques for Digital Hearing Aids

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 15-Nov-2018
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319968216
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Speech Enhancement Techniques for Digital Hearing AidsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 15-Nov-2018
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319968216
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This book provides various speech enhancement algorithms for digital hearing aids. It covers information on noise signals extracted from silences of speech signal. The description of the algorithm used for this purpose is also provided. Different types of adaptive filters such as Least Mean Squares (LMS), Normalized LMS (NLMS) and Recursive Lease Squares (RLS) are described for noise reduction in the speech signals. Different types of noises are taken to generate noisy speech signals, and therefore information on various noises signals is provided. The comparative performance of various adaptive filters for noise reduction in speech signals is also described. In addition, the book provides a speech enhancement technique using adaptive filtering and necessary frequency strength enhancement using wavelet transform as per the requirement of audiogram for digital hearing aids.













Presents speech enhancement techniques for improving performance of digital hearing aids;

Covers various types of adaptive filters and their advantages and limitations;

Provides a hybrid speech enhancement technique using wavelet transform and adaptive filters.
1 Introduction
1(12)
1.1 Sound
1(1)
1.2 Ear Structure and Its Workings
1(2)
1.2.1 External Ear
2(1)
1.2.2 Middle Ear
2(1)
1.2.3 Inner Ear
2(1)
1.2.4 Cochlea
3(1)
1.3 Hearing Impaired
3(1)
1.4 Audiogram
4(1)
1.5 Digital Hearing Aids
4(3)
1.6 Issues in Digital Hearing Aids
7(1)
1.7 Motivation for This Book
7(4)
1.7.1 Important Areas of Speech Signal Covered in This Book
9(2)
1.8 Book Organization
11(2)
References
11(2)
2 Generation of Speech Signal and Its Characteristics
13(16)
2.1 Speech Signal
13(5)
2.1.1 Articulatory Phonetics and Speech Generation
13(1)
2.1.2 Anatomy and Physiology of Speech Generation
14(1)
2.1.3 Vocal Tract
14(2)
2.1.4 Larynx and Vocal Folds or Cords
16(2)
2.2 Major Features of Speech Articulation
18(2)
2.3 Properties and Characteristics of Speech Signal
20(9)
2.3.1 Time and Frequency Domain Characteristics of Speech
21(1)
2.3.2 Waveforms
21(1)
2.3.3 Fundamental Frequency
21(1)
2.3.4 Overall Power
22(1)
2.3.5 Overall Frequency Spectrum
22(1)
2.3.6 Short-Time Energy
23(1)
2.3.7 Spectrogram
23(1)
2.3.8 Short-Time Average Zero Crossing Rate
24(3)
References
27(2)
3 Introduction of Adaptive Filters and Noises for Speech
29(34)
3.1 Adaptive Filter
29(1)
3.2 LMS Adaptive Filter
30(11)
3.2.1 Least Mean Square Adaptation Algorithm
33(2)
3.2.2 Statistical LMS Theory
35(1)
3.2.3 Direct Averaging Method
36(1)
3.2.4 Small Step Size Statistical Theory
37(1)
3.2.5 Natural Modes of the LMS Filter
38(1)
3.2.6 Learning Curves for Adaptive Algorithms
39(1)
3.2.7 Comparison of the LMS Algorithm with the Steepest Descent Algorithm
40(1)
3.3 Normalized Least Mean Square (NLMS) Adaptive Filter
41(7)
3.3.1 Structure and Operation of NLMS
42(3)
3.3.2 Stability of the Normalized LMS Filter
45(1)
3.3.3 Special Environment of Real Valued Data
46(2)
3.4 Recursive Least Squares (RLS) Adaptive Filter
48(9)
3.4.1 Regularization
49(1)
3.4.2 Reformulation of the Normal Equations
50(1)
3.4.3 Recursive Computations of O(n) and z(n)
50(1)
3.4.4 The Matrix Inversion Lemma
51(2)
3.4.5 Selection of the Regularization Parameter
53(1)
3.4.6 Convergence Analysis of RLS Algorithm
54(1)
3.4.7 Convergence of the RLS Algorithm in the Mean Value
55(1)
3.4.8 Mean Square Deviation of the RLS Algorithm
56(1)
3.4.9 Ensemble Average Learning Curve of the RLS Algorithm
57(1)
3.5 Noise
57(6)
3.5.1 Sources of Noise
59(2)
References
61(2)
4 Fourier Transform, Short-Time Fourier Transform, and Wavelet Transform
63(12)
4.1 Fourier Transform (FT)
63(1)
4.2 Short-Time FT
63(1)
4.3 Wavelet Transform (WT)
64(3)
4.4 Comparison of the Wavelet Transform (WT) with FT and STFT
67(4)
4.5 Multiresolution Algorithm
71(4)
References
74(1)
5 Speech Signal Enhancement Using Adaptive Filters
75(50)
5.1 Introduction
75(1)
5.2 Steps for Speech Enhancement Process
76(1)
5.3 Implementation Flow of VAD Algorithm
76(1)
5.4 Speech Enhancement Process based on LMS Algorithm
77(18)
5.4.1 Results for White Noise Signal
81(5)
5.4.2 Results for Babble Noise Signal
86(5)
5.4.3 Results for Traffic Jam Noise Signal
91(4)
5.5 Speech Enhancement Process Based on the NLMS Algorithm
95(14)
5.5.1 Results for White Noise Signal
98(4)
5.5.2 Results for Babble Noise Signal
102(4)
5.5.3 Results for Traffic Jam Noise Signal
106(3)
5.6 Speech Enhancement Process Based on the RLS Algorithm
109(14)
5.6.1 Results for White Noise Signal
110(5)
5.6.2 Results for Babble Noise Signal
115(4)
5.6.3 Results for Traffic Jam Noise Signal
119(4)
5.7 Comparative Analysis of Simulation Results
123(2)
References
124(1)
6 Speech Signal Enhancement Based on Wavelet Transform
125(24)
6.1 Procedure for Speech Signal Enhancement Using Wavelet Transform
125(5)
6.2 Implementation and Results of Speech Signal Enhancement Using Wavelet Transform
130(19)
6.2.1 First Band Enhancement
131(2)
6.2.2 Second Band Enhancement
133(1)
6.2.3 Third Band Enhancement
134(3)
6.2.4 Fourth Band Enhancement
137(1)
6.2.5 Fifth Band Enhancement
138(2)
6.2.6 Sixth Band Enhancement
140(2)
6.2.7 Seventh Band Enhancement
142(1)
6.2.8 Eighth Band Enhancement
143(2)
6.2.9 Ninth Band Enhancement
145(3)
References
148(1)
7 Summary of This Book and Future Research Directions
149(2)
7.1 Important Points Covered in the Book
149(1)
7.2 Future Research Direction
150(1)
Index 151
Dr. Komal R. Borisagar has obtained her Ph.D. in Speech Enhancement Techniques for Digital Hearing Aids in 2012. She has teaching experience of over 15 years. She is working as an Associate Professor at Electronics & Communication Department, Atmiya Institute of Technology and Science, Rajkot. She has published 2 books, 3 book chapters and more than 50 research papers to her credit in referred & indexed journals, conferences at international and in IEEE digital library. She has achieved best paper award five times for her research articles and presentation. Her areas of interest are wireless communication, speech processing, signal & system and image processing.





 Dr. Rohit M. Thanki has obtained his Ph.D. in Multibiometric System Security using CS Theory and Watermarking from C. U. Shah University, Wadhwan city, Gujarat, India in 2017. His area of research is Digital Watermarking, Biometrics System, Security, Compressive Sensing, Pattern Recognition and Image Processing. He has published 7 books, 9 book chapters and more than 25 research papers to his credit in refereed & indexed journals, and conferences at international and national level. He has achieved best paper presentation awards 2 times for his research articles. 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.





Dr. Bhavin S. Sedani is working as a professor in Electronics and Communication Department at L.D. College of Engineering, Ahmedabad. He has teaching experience of 16 years. He has presented   more than 46 research papers in various international and national conferences. His 21 research papers are published in various international journals and IEEE Xplore. He has achieved best paper presentation awards 7 times for his research articles. He is awarded with pedagogical award for continuing efforts towards teaching learning methodology, research and innovation in 2017 by Gujarat Technological University, Young Researcher Award in 2018 by Integrated Intelligent Research, ISTE Professional Center, Anna University Campus, Chennai India. His area of research is in wireless communication and speech processing.
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