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Other titles in the Prentice-Hall Series in Signal Processing series:
Discrete-Time Speech Signal Processing: Principles and Practice (Prentice-Hall Series in Signal Processing)
Synopses & Reviews
Essential principles, practical examples, current applications, and leading-edge research.
In this book, Thomas F. Quatieri presents the field's most intensive, up-to-date tutorial and reference on discrete-time speech signal processing. Building on his MIT graduate course, he introduces key principles, essential applications, and state-of-the-art research, and he identifies limitations that point the way to new research opportunities.
Quatieri provides an excellent balance of theory and application, beginning with a complete framework for understanding discrete-time speech signal processing. Along the way, he presents important advances never before covered in a speech signal processing text book, including sinusoidal speech processing, advanced time-frequency analysis, and nonlinear aeroacoustic speech production modeling. Coverage includes:
The book's in-depth applications coverage includes speech coding, enhancement, and modification; speaker recognition; noise reduction; signal restoration; dynamic range compression, and more. Principles of Discrete-Time Speech Processing also contains an exceptionally complete series of examples and Matlab exercises, all carefully integrated into the book's coverage of theory and applications.
Quatieri presents the field's most intensive, up-to-date tutorial and reference on discrete-time speech signal processing. Building on his MIT graduate course, he introduces key principles, essential applications, and state-of-the-art research, and he identifies limitations that point the way to new research opportunities.
About the Author
THOMAS F. QUATIERI is a Senior Member of the Technical Staff at MIT's Lincoln Laboratory in Lexington, MA. He is involved in digital signal processing for speech and audio modification, coding, enhancement, and speaker recognition, and he developed MIT's graduate course in Digital Speech Processing. Quatieri's publications and papers include Speech Analysis/Synthesis Based on a Sinusoidal Representation, winner of the 1990 IEEE Signal Processing Society's Senior Award, and Energy Separation in Signal Modulations with Application to Speech Analysis, winner of the 1994 IEEE Signal Processing Society's Senior Award and the 1995 IEEE W.R.G. Baker Prize Award. A Fellow of the IEEE and a member of Sigma Xi, Tau Beta Pi, Eta Kappa Nu, and the Acoustical Society of America, he holds S.M., E.E., and Sc.D. degrees from MIT.
Table of Contents
(NOTE: Each chapter begins with an introduction and concludes with a Summary, Exercises and Bibliography.)
Discrete-Time Speech Signal Processing. The Speech Communication Pathway. Analysis/Synthesis Based on Speech Production and Perception. Applications. Outline of Book.
2. A Discrete-Time Signal Processing Framework.
Discrete-Time Signals. Discrete-Time Systems. Discrete-Time Fourier Transform. Uncertainty Principle. z-Transform. LTI Systems in the Frequency Domain. Properties of LTI Systems. Time-Varying Systems. Discrete-Fourier Transform. Conversion of Continuous Signals and Systems to Discrete Time.
3. Production and Classification of Speech Sounds.
Anatomy and Physiology of Speech Production. Spectrographic Analysis of Speech. Categorization of Speech Sounds. Prosody: The Melody of Speech. Speech Perception.
4. Acoustics of Speech Production.
Physics of Sound. Uniform Tube Model. A Discrete-Time Model Based on Tube Concatenation. Vocal Fold/Vocal Tract Interaction.
5. Analysis and Synthesis of Pole-Zero Speech Models.
Time-Dependent Processing. All-Pole Modeling of Deterministic Signals. Linear Prediction Analysis of Stochastic Speech Sounds. Criterion of “Goodness”. Synthesis Based on All-Pole Modeling. Pole-Zero Estimation. Decomposition of the Glottal Flow Derivative.
Appendix 5.A: Properties of Stochastic Processes.
Random Processes. Ensemble Averages. Stationary Random Process. Time Averages. Power Density Spectrum.
Appendix 5.B: Derivation of the Lattice Filter in Linear Prediction Analysis.
6. Homomorphic Signal Processing.
Concept. Homomorphic Systems for Convolution. Complex Cepstrum of Speech-Like Sequences. Spectral Root Homomorphic Filtering. Short-Time Homomorphic Analysis of Periodic Sequences. Short-Time Speech Analysis. Analysis/Synthesis Structures. Contrasting Linear Prediction and Homomorphic Filtering.
7. Short-Time Fourier Transform Analysis and Synthesis.
Short-Time Analysis. Short-Time Synthesis. Short-Time Fourier Transform Magnitude. Signal Estimation from the Modified STFT or STFTM. Time-Scale Modification and Enhancement of Speech.
Appendix 7.A: FBS Method with Multiplicative Modification.
8. Filter-Bank Analysis/Synthesis.
Revisiting the FBS Method. Phase Vocoder. Phase Coherence in the Phase Vocoder. Constant-Q Analysis/Synthesis. Auditory Modeling.
9. Sinusoidal Analysis/Synthesis.
Sinusoidal Speech Model. Estimation of Sinewave Parameters. Synthesis. Source/Filter Phase Model. Additive Deterministic-Stochastic Model.
Appendix 9.A: Derivation of the Sinewave Model.
Appendix 9.B: Derivation of Optimal Cubic Phase Parameters.
10. Frequency-Domain Pitch Estimation.
A Correlation-Based Pitch Estimator. Pitch Estimation Based on a “Comb Filter<170. Pitch Estimation Based on a Harmonic Sinewave Model. Glottal Pulse Onset Estimation. Multi-Band Pitch and Voicing Estimation.
11. Nonlinear Measurement and Modeling Techniques.
The STFT and Wavelet Transform Revisited. Bilinear Time-Frequency Distributions. Aeroacoustic Flow in the Vocal Tract. Instantaneous Teager Energy Operator.
12. Speech Coding.
Statistical Models of Speech. Scaler Quantization. Vector Quantization (VQ). Frequency-Domain Coding. Model-Based Coding. LPC Residual Coding.
13. Speech Enhancement.
Introduction. Preliminaries. Wiener Filtering. Model-Based Processing. Enhancement Based on Auditory Masking.
Appendix 13.A: Stochastic-Theoretic parameter Estimation.
14. Speaker Recognition.
Introduction. Spectral Features for Speaker Recognition. Speaker Recognition Algorithms. Non-Spectral Features in Speaker Recognition. Signal Enhancement for the Mismatched Condition. Speaker Recognition from Coded Speech.
Appendix 14.A: Expectation-Maximization (EM) Estimation.
Glossary.Speech Signal Processing.Units.Databases.Index.About the Author.
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