The Stationary Bionic Wavelet Transform and its Applications for ECG and Speech Processing (Signals and Communication Technology)

Book Summary
Introduction
Contents
About the Author
Acronyms
Chapter 1: Speech Enhancement Based on Stationary Bionic Wavelet Transform and Maximum A Posterior Estimator of Magnitude-Squared Spectrum
	1.1 Introduction
	1.2 The Proposed Technique
		1.2.1 Background
			1.2.1.1 Wavelet Analysis [21, 22]
			1.2.1.2 The Bionic Wavelet Transform
			1.2.1.3 The Stationary Bionic Wavelet Transform (SBWT)
				The Stationary Wavelet Transform (SWT)
				Perfect Reconstruction of SBWT
	1.3 Application of the Maximum A Posterior Estimator of Magnitude-Squared Spectrum in SBWT Domain
	1.4 The Evaluation Metrics
		1.4.1 Signal-to-Noise Ratio
		1.4.2 Segmental Signal-to-Noise Ratio
		1.4.3 Itakura–Saito Distance
		1.4.4 Perceptual Evaluation of Speech Quality (PESQ)
	1.5 Results and Discussions
	1.6 Conclusion
	References
Chapter 2: ECG Denoising Based on 1-D Double-Density Complex DWT and SBWT
	2.1 Introduction
	2.2 Materials
		2.2.1 The BWT Optimization for ECG Analysis
		2.2.2 1-D Double-Density Complex DWT
		2.2.3 Denoising Technique Based on Wavelets and Hidden Markov Models
		2.2.4 The Denoising Approach Based on Non-local Means
		2.2.5 The ECG Denoising Approach Based on BWT and FWT_TI [49]
		2.2.6 The Proposed ECG Denoising Approach [29]
	2.3 Results and Discussion
	2.4 Conclusion
	References
Chapter 3: Speech Enhancement Based on SBWT and MMSE Estimate of Spectral Amplitude
	3.1 Introduction
	3.2 The MMSE Estimate of Spectral Amplitude
		3.2.1 Signal Model
	3.3 The Proposed Speech Enhancement Approach [19]
	3.4 Minimum Mean Square Error (MMSE) Estimate of Spectral Amplitude in the SBWT Domain
	3.5 Unsupervised Speech Denoising Via Perceptually Motivated Robust Principal Component Analysis [23]
	3.6 The Speech Enhancement Technique Based on MSS–SMPO [25]
	3.7 Results and Discussion
	3.8 Conclusion
	References
Chapter 4: Arabic Speech Recognition by Stationary Bionic Wavelet Transform and MFCC Using a Multi-layer Perceptron for Voice Control
	4.1 Introduction
	4.2 The Feature Extraction
		4.2.1 MFCC Extraction
	4.3 Pre-emphasis
	4.4 Frame Blocking and Windowing
	4.5 DFT Spectrum
	4.6 Mel Spectrum
	4.7 Discrete Cosine Transform (DCT)
	4.8 Dynamic MFCC Features
	4.9 Classifiers
	4.10 The Proposed Speech Recognition Technique [12]
	4.11 Experiments and Results
	4.12 Conclusion
	References
Index