Digital Signal Processing, 4th Edition

A significant revision of a best-selling text for the introductory digital signal processing course. This book presents the fundamentals of discrete-time signals, systems, and modern digital processing and applications for students in electrical engineering, computer engineering, and computer science.The book is suitable for either a one-semester or a two-semester undergraduate level course in discrete systems and digital signal processing. It is also intended for use in a one-semester first-year graduate-level course in digital signal processing.

1 Introduction 1.1 Signals, Systems, and Signal Processing 1.2 Classification of Signals 1.3 The Concept of Frequency in Continuous-Time and Discrete-Time Signals 1.4 Analog-to-Digital and Digital-to-Analog Conversion 1.5 Summary and References 2 Discrete-Time Signals and Systems 2.1 Discrete-Time Signals 2.2 Discrete-Time Systems 2.3 Analysis of Discrete-Time Linear Time-Invariant Systems 2.4 Discrete-Time Systems Described by Difference Equations 2.5 Implementation of Discrete-Time Systems 2.6 Correlation of Discrete-Time Signals 2.7 Summary and References 3 The z-Transform and Its Application to the Analysis of LTI Systems 3.1 The z-Transform 3.2 Properties of the z-Transform 3.3 Rational z-Transforms 3.4 Inversion of the z-Transform 3.5 Analysis of Linear Time-Invariant Systems in the z-Domain 3.6 The One-sided z-Transform 3.7 Summary and References 4 Frequency Analysis of Signals 4.1 Frequency Analysis of Continuous-Time Signals 4.2 Frequency Analysis of Discrete-Time Signals 4.3 Frequency-Domain and Time-Domain Signal Properties 4.4 Properties of the Fourier Transform for Discrete-Time Signals 4.5 Summary and References 5.1 Frequency-Domain Characteristics of Linear Time-Invariant Systems 5.2 Frequency Response of LTI Systems 5.3 Correlation Functions and Spectra at the Output of LTI Systems 5.4 Linear Time-Invariant Systems as Frequency-Selective Filters 5.5 Inverse Systems and Deconvolution 5.6 Summary and References 6 Sampling and Reconstruction of Signals 6.1 Ideal Sampling and Reconstruction of Continuous-Time Signals 6.2 Discrete-Time Processing of Continuous-Time Signals 6.3 Analog-to-Digital and Digital-to-Analog Converters 6.4 Sampling and Reconstruction of Continuous-Time Bandpass Signals 6.5 Sampling of Discrete-Time Signals 6.6 Oversampling A/D and D/A Converters 6.7 Summary and References 7 The Discrete Fourier Transform: Its Properties and Applications 7.1 Frequency-Domain Sampling: The Discrete Fourier Transform 7.2 Properties of the DFT 7.3 Linear Filtering Methods Based on the DFT 7.4 Frequency Analysis of Signals Using the DFT 7.5 The Discrete Cosine Transform 7.6 Summary and References 8 Efficient Computation of the DFT: Fast Fourier Transform Algorithms 8.1 Efficient Computation of the DFT: FFT Algorithms 8.2 Applications of FFT Algorithms 8.3 A Linear Filtering Approach to Computation of the DFT 8.4 Quantization Effects in the Computation of the DFT 8.5 Summary and References 9 Implementation of Discrete-Time Systems 9.1 Structures for the Realization of Discrete-Time Systems 9.2 Structures for FIR Systems 9.3 Structures for IIR Systems 9.4 Representation of Numbers 9.5 Quantization of Filter Coefficients 9.6 Round-Off Effects in Digital Filters 9.7 Summary and References 10 Design of Digital Filters 10.1 General Considerations 10.2 Design of FIR Filters 10.3 Design of MR Filters From Analog Filters 10.4 Frequency Transformations 10.5 Summary and References 11 Multirate Digital Signal Processing 11.1 Introduction 11.2 Decimation by a Factor D 11.3 Interpolation by a Factor I 11.4 Sampling Rate Conversion by a Rational Factor I/D 11.5 Implementation of Sampling Rate Conversion 11.6 Multistage Implementation of Sampling Rate Conversion 11.7 Sampling Rate Conversion of Bandpass Signals 11.8 Sampling Rate Conversion by an Arbitrary Factor 11.9 Applications of Multirate Signal Processing 11.10 Digital Filter Banks 11.11 Two-Channel Quadrature Mirror Filter Bank 11.12 M-Channel QMF Bank 11.13 Summary and References 12 Linear Prediction and Optimum Linear Filters 12.1 Random Signals, Correlation Functions, and Power Spectra 12.2 Innovations Representation of a Stationary Random Process 12.3 Forward and Backward Linear Prediction 12.4 Solution of the Normal Equations 12.5 Properties of the Linear Prediction-Error Filters 12.6 AR Lattice and ARMA Lattice-Ladder Filters 12.7 Wiener Filters for Filtering and Prediction 12.8 Summary and References 13 Adaptive Filters 13.1 Applications of Adaptive Filters 13.2 Adaptive Direct-Form FIR Filters—The LMS Algorithm 13.3 Adaptive Direct-Form Filters—RLS Algorithms 13.4 Adaptive Lattice-Ladder Filters 13.5 Summary and References 14 Power Spectrum Estimation 14.1 Estimation of Spectra from Finite-Duration Observations of Signals 14.2 Nonparametric Methods for Power Spectrum Estimation 14.3 Parametric Methods for Power Spectrum Estimation 14.4 Filter Bank Methods 14.5 Eigenanalysis Algorithms for Spectrum Estimation 14.6 Summary and References Appendix A: Random Number Generators Appendix B: Tables of Transition Coefficients for the Design of Linear-Phase FIR Filters References and Bibliography Answers to Selected Problems Index

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