"Mathematical Modeling and Signal Processing in Speech and Hearing Sciences" by Jack Xin, ‎Yingyong Qi (Repost)

The aim of the book is to give an accessible introduction of mathematical models and signal processing methods in speech and hearing sciences for senior undergraduate and beginning graduate students with basic knowledge of linear algebra, differential equations, numerical analysis, and probability.

Speech and hearing sciences are fundamental to numerous technological advances of the digital world in the past decade, from music compression in MP3 to digital hearing aids, from network based voice enabled services to speech interaction with mobile phones.
Mathematics and computation are intimately related to these leaps and bounds. On the other hand, speech and hearing are strongly interdisciplinary areas where dissimilar scientific and engineering publications and approaches often coexist and make it difficult for newcomers to enter.

Contents
Preface
1 Background Signal Processing, Statistical and Optimization Methods
1.1 Introduction
1.2 Fourier and z-Transforms
1.3 Filtering and Convolution
1.4 Random Variables, Correlation and Independence
1.5 Data Clustering and K-Means Method
1.6 Maximum Likelihood Method
1.7 Least Squares and Sparse Optimization Methods
1.8 Exercises
2 Speech Modeling
2.1 Introduction
2.2 Two Mass Vocal Fold Model
2.3 Matlab Program and Animation of Two Mass Model
2.4 Hydrodynamic Semi-Continuum Vocal Fold Model
2.5 Source-Filter Model of Speech Production
2.6 Exercises
3 Auditory Modeling
3.1 Introduction
3.2 Macromechanics and Passive Models
3.3 Micromechanics and Two Level Nonlocal Active Models
3.4 Dispersion and Decay Properties of Plane Waves
3.5 Time Harmonic Solutions
3.6 Asymptotic and Transform Techniques
3.7 Logarithmic Scales and Critical Bands
3.8 Time Demain Method and Dispersive Instability
3.9 Boundary Integral Method and Suppression of Instability
3.10 Computational Methods of Nonlocal Active Models
3.11 Nonlinear Phenomena and Sound Masking
3.12 Invertible Auditory Transforms
3.13 Orthogonal Auditory Transforms
3.14 Modeling Masking Thresholds
3.15 Modeling Hearing Loss and Hearing Aids
3.16 Matlab Programs
3.17 Exercises
4 Speech Recognition
4.1 Introduction
4.2 Hidden Markov Model (HMM) for Speech Processing
4.3 HMM for Isolated Word Recognition
4.4 Summary of Matlab Programs
4.5 Chapter Summary
4.6 Matlab Programs
4.7 Exercises
5 Blind Source Separation and Speech Enhancement
5.1 Introduction
5.2 Instantaneous Mixture and Decorrelation Methods
5.3 Instantaneous Mixture and Cumulant Method
5.4 Instantaneous Mixture and Infomax Methods
5.5 Convolutive Mixture and Decorrelation Method
5.6 Convolutive Mixture and Infomax Methods
5.7 Relative Sparsity and Time-Frequency Domain Methods
5.8 Convex Speech Enhancement Model
5.9 Summary and Other Methods for Further Reading
5.10 Matlab Programs
5.11 Exercises
5 Blind Source Separation and Speech Enhancement
References
Index