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All India Institute of Speech and Hearing, Mysuru
(An Autonomous institute Under Ministry of Health and Family Welfare, Government of India.)

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Mathematical modeling and signal processing in speech and hearing sciences / Jack Xin, Yingyong Qi.

By: Contributor(s): Material type: TextSeries: MS&A (Series) ; v. 10.Publisher: Cham : Springer, [2014]Description: xi, 206 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9783319030852 (hbk. : alk. paper)
  • 331903085X (hbk. : alk. paper)
Subject(s): DDC classification:
  • 006.454 XIN Q.10 23
LOC classification:
  • TK5102.9 .X56 2014
Contents:
Machine generated contents note: 1.Background Signal Processing, Statistical and Optimization Methods -- 1.1.Introduction -- 1.2.Fourier and z-Transforms -- 1.2.1.Continuous Time Signals -- 1.2.2.Fourier Transform and Basic Properties -- 1.2.3.Discrete Time Signals and Systems -- 1.2.4.Sampling and Shannon Theory -- 1.2.5.Discrete Fourier Transform -- 1.2.6.Discrete Time and Windowed Fourier Transforms -- 1.2.7.Short Time Fourier Transform, Synthesis and Spectrogram -- 1.2.8.z-Transform -- 1.3.Filtering and Convolution -- 1.3.1.Circular Convolution -- 1.3.2.Linear Convolution and z-Transform -- 1.3.3.Circular Convolution and z-Transform -- 1.3.4.Rational Filters, Impulse and Frequency Responses -- 1.3.5.Group and Phase Delays -- 1.3.6.Minimum Phase and All Pass Filters -- 1.4.Random Variables, Correlation and Independence -- 1.4.1.Basic Notion and Examples -- 1.4.2.Joint Distribution and Independent Components -- 1.4.3.Random Number Generation -- 1.4.4.Stochastic Processes -- 1.4.5.Random Walk and Brownian Motion -- 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 -- 2.5.1.Uniform Lossless Tube Model and Transfer Function -- 2.5.2.Concatenated Lossless Tube Model: Traveling Waves and Transfer Function -- 2.5.3.Radiation and the Complete Model -- 2.5.4.Matlab Programs for Vowel and Consonant Synthesis -- 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 -- 4.2.1.Speech Spectral Analysis -- 4.2.2.Vector Quantization -- 4.3.HMM for Isolated Word Recognition -- 4.3.1.Forward and Backward Probabilities -- 4.3.2.Baum-Welch Re-Estimation -- 4.3.3.Viterbi Decoding -- 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 -- 5.2.1.Decorrelation with Second Order Statistics -- 5.2.2.Demixing with Joint Second and Third Order Statistics -- 5.3.Instantaneous Mixture and Cumulant Method -- 5.3.1.Moments and Cumulants -- 5.3.2.Source Recovery and Whitening Process -- 5.3.3.Unitary Factor as Joint Diagonalizer of Cumulant Matrices -- 5.3.4.Joint Diagonalization of Eigenmatrices -- 5.3.5.Jacobi Method and Joint Diagonalizer Formula -- 5.4.Instantaneous Mixture and Infomax Methods -- 5.4.1.Statistical Equations for Source Separation -- 5.4.2.Iterative Methods -- 5.4.3.Uniform Bounds -- 5.4.4.Convergence and Source Separation -- 5.4.5.Numerical Example -- 5.5.Convolutive Mixture and Decorrelation Method -- 5.5.1.Decorrelation Equations -- 5.5.2.Constrained and Penalized Optimization -- 5.5.3.Numerical Example -- 5.6.Convolutive Mixture and Infomax Methods -- 5.6.1.Extensions and Analysis of Algorithms -- 5.6.2.Numerical Example -- 5.7.Relative Sparsity and Time-Frequency Domain Methods -- 5.8.Convex Speech Enhancement Model -- 5.8.1.Convex Model and l1 Regularization -- 5.8.2.Minimization by Bregman Method -- 5.9.Summary and Other Methods for Further Reading -- 5.10.Matlab Programs -- 5.11.Exercises.
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Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
Reference text books All India Institute of Speech and Hearing R-1/5 006.454 XIN Q.10 (Browse shelf(Opens below)) Available 19733
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Includes bibliographical references (pages [189]-197) and index.

Machine generated contents note: 1.Background Signal Processing, Statistical and Optimization Methods -- 1.1.Introduction -- 1.2.Fourier and z-Transforms -- 1.2.1.Continuous Time Signals -- 1.2.2.Fourier Transform and Basic Properties -- 1.2.3.Discrete Time Signals and Systems -- 1.2.4.Sampling and Shannon Theory -- 1.2.5.Discrete Fourier Transform -- 1.2.6.Discrete Time and Windowed Fourier Transforms -- 1.2.7.Short Time Fourier Transform, Synthesis and Spectrogram -- 1.2.8.z-Transform -- 1.3.Filtering and Convolution -- 1.3.1.Circular Convolution -- 1.3.2.Linear Convolution and z-Transform -- 1.3.3.Circular Convolution and z-Transform -- 1.3.4.Rational Filters, Impulse and Frequency Responses -- 1.3.5.Group and Phase Delays -- 1.3.6.Minimum Phase and All Pass Filters -- 1.4.Random Variables, Correlation and Independence -- 1.4.1.Basic Notion and Examples -- 1.4.2.Joint Distribution and Independent Components -- 1.4.3.Random Number Generation -- 1.4.4.Stochastic Processes -- 1.4.5.Random Walk and Brownian Motion -- 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 -- 2.5.1.Uniform Lossless Tube Model and Transfer Function -- 2.5.2.Concatenated Lossless Tube Model: Traveling Waves and Transfer Function -- 2.5.3.Radiation and the Complete Model -- 2.5.4.Matlab Programs for Vowel and Consonant Synthesis -- 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 -- 4.2.1.Speech Spectral Analysis -- 4.2.2.Vector Quantization -- 4.3.HMM for Isolated Word Recognition -- 4.3.1.Forward and Backward Probabilities -- 4.3.2.Baum-Welch Re-Estimation -- 4.3.3.Viterbi Decoding -- 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 -- 5.2.1.Decorrelation with Second Order Statistics -- 5.2.2.Demixing with Joint Second and Third Order Statistics -- 5.3.Instantaneous Mixture and Cumulant Method -- 5.3.1.Moments and Cumulants -- 5.3.2.Source Recovery and Whitening Process -- 5.3.3.Unitary Factor as Joint Diagonalizer of Cumulant Matrices -- 5.3.4.Joint Diagonalization of Eigenmatrices -- 5.3.5.Jacobi Method and Joint Diagonalizer Formula -- 5.4.Instantaneous Mixture and Infomax Methods -- 5.4.1.Statistical Equations for Source Separation -- 5.4.2.Iterative Methods -- 5.4.3.Uniform Bounds -- 5.4.4.Convergence and Source Separation -- 5.4.5.Numerical Example -- 5.5.Convolutive Mixture and Decorrelation Method -- 5.5.1.Decorrelation Equations -- 5.5.2.Constrained and Penalized Optimization -- 5.5.3.Numerical Example -- 5.6.Convolutive Mixture and Infomax Methods -- 5.6.1.Extensions and Analysis of Algorithms -- 5.6.2.Numerical Example -- 5.7.Relative Sparsity and Time-Frequency Domain Methods -- 5.8.Convex Speech Enhancement Model -- 5.8.1.Convex Model and l1 Regularization -- 5.8.2.Minimization by Bregman Method -- 5.9.Summary and Other Methods for Further Reading -- 5.10.Matlab Programs -- 5.11.Exercises.

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