Digital Signal Processing: Theory and Practice

DIGITAL SIGNAL PROCESSING Understand the future of signal processing with the latest edition of this groundbreaking text Signal processing is a key aspect of virtually all engineering fields. Digital techniques enormously expand the possible applications of signal processing, forming a part of not only conventional engineering projects but also data analysis and artificial intelligence. There are considerable challenges raised by these techniques, however, as the gulf between theory and practice can be wide; the successful integration of digital signal processing techniques requires engineers capable of bridging this gulf. For years, Digital Signal Processing has met this need with a comprehensive guide that consistently connects abstract theory with practical applications. Now fully updated to reflect the most recent developments in this crucial field, the tenth* edition of this seminal text promises to foster a broader understanding of signal processing among a new generation of engineers and researchers. Readers of the new edition of Digital Signal Processing will also find: Exercises at the end of each chapter to reinforce key concepts A new chapter covering digital signal processing for neural networks Handy structure beginning with undergraduate-level material before moving to more advanced concepts in the second half Digital Signal Processing is a must-own for students, researchers, and industry professionals in any of the hundreds of fields and subfields that make use of signal processing algorithms. *This is the English language translation of the French original Traitement Numérique du Signal 10th edition by Maurice Bellanger © Dunod 2022 and is the 4th edition in English.

Table of Contents:
Foreword (Historical Perspective) xi Preface xiii Introduction xv 1 Signal Digitizing - Sampling and Coding 1 1.1 Fourier Analysis 1 1.2 Distributions 4 1.3 Some Commonly Studied Signals 6 1.4 The Norms of a Function 12 1.5 Sampling 13 1.6 Frequency Sampling 14 1.7 The Sampling Theorem 15 1.8 Sampling of Sinusoidal and Random Signals 16 1.9 Quantization 20 1.10 The Coding Dynamic Range 22 1.11 Nonlinear Coding with the 13-segment A-law 24 1.12 Optimal Coding 26 1.13 Quantity of Information and Channel Capacity 28 1.14 Binary Representations 29 2 The Discrete Fourier Transform 35 2.1 Definition and Properties of the Discrete Fourier Transform 36 2.2 Fast Fourier Transform (FFT) 38 2.3 Degradation Arising fromWordlength Limitation Effects 45 2.4 Calculation of a Spectrum Using the DFT 46 2.5 Fast Convolution 50 2.6 Calculations of a DFT Using Convolution 51 2.7 Implementation 52 3 Other Fast Algorithms for the FFT 55 3.1 Kronecker Product of Matrices 55 3.2 Factorizing the Matrix of a Decimation-in-Frequency Algorithm 56 3.3 Partial Transforms 58 3.4 Lapped Transform 66 3.5 Other Fast Algorithms 67 3.6 Binary Fourier Transform - Hadamard 71 3.7 Number-Theoretic Transforms 71 4 Time-Invariant Discrete Linear Systems 77 4.1 Definition and Properties 77 4.2 The Z-Transform 78 4.3 Energy and Power of Discrete Signals 80 4.4 Filtering of Random Signals 82 4.5 Systems Defined by Difference Equations 83 4.6 State Variable Analysis 85 5 Finite Impulse Response (FIR) Filters 89 5.1 FIR Filters 89 5.2 Practical Transfer Functions and Linear Phase Filters 91 5.3 Calculation of Coefficients by Fourier Series Expansion for Frequency Specifications 94 5.4 Calculation of Coefficients by the Least-Squares Method 97 5.5 Calculation of Coefficient by Discrete Fourier Transform 99 5.6 Calculation of Coefficients by Chebyshev Approximation 100 5.7 Relationships Between the Number of Coefficients and the Filter Characteristic 102 5.8 Raised-Cosine Transition Filter 104 5.9 Structures for Implementing FIR Filters 106 5.10 Limitation of the Number of Bits for Coefficients 107 5.11 Z-Transfer Function of an FIR Filter 109 5.12 Minimum-Phase Filters 111 5.13 Design of Filters with a Large Number of Coefficients 113 5.14 Two-Dimensional FIR Filters 114 5.15 Coefficients of Two-Dimensional FIR Filters by the Least-Squares Method 118 6 Infinite Impulse Response (IIR) Filter Sections 123 6.1 First-Order Section 123 6.2 Purely Recursive Second-Order Section 127 6.3 General Second-Order Section 134 6.4 Structures for Implementation 138 6.5 CoefficientWordlength Limitation 140 6.6 Internal DataWordlength Limitation 141 6.7 Stability and Limit Cycles 142 7 Infinite Impulse Response Filters 147 7.1 General Expressions for the Properties of IIR Filters 147 7.2 Direct Calculations of the Coefficients Using Model Functions 148 8 Digital Ladder Filters 173 8.1 Properties of Two-Port Circuits 173 8.2 Simulated Ladder Filters 176 8.3 Switched-Capacitor Filters 180 8.4 Lattice Filters 183 8.5 Comparison Elements 187 9 Complex Signals - Quadrature Filters - Interpolators 189 9.1 The Fourier Transform of a Real and Causal Set 189 9.2 Analytic Signals 192 9.3 Calculating the Coefficients of an FIR Quadrature Filter 195 9.4 Recursive 90° Phase Shifters 197 9.5 Single Side-Band Modulation 199 9.6 Minimum-Phase Filters 200 9.7 Differentiator 201 9.8 Interpolation Using FIR Filters 202 9.9 Lagrange Interpolation 203 9.10 Interpolation by Blocks - Splines 204 9.11 Interpolations and Signal Restoration 206 9.12 Conclusion 208 10 Multirate Filtering 213 10.1 Decimation and Z-Transform 213 10.2 Decomposition of a Low-Pass FIR Filter 217 10.3 Half-Band FIR Filters 220 10.4 Decomposition with Half-Band Filters 222 10.5 Digital Filtering by Polyphase Network 224 10.6 Multirate Filtering with IIR Elements 227 10.7 Filter Banks Using Polyphase Networks and DFT 227 10.8 Conclusion 229 11 QMF Filters and Wavelets 233 11.1 Decomposition into Two Sub-Bands and Reconstruction 233 11.2 QMF Filters 233 11.3 Perfect Decomposition and Reconstruction 236 11.4 Wavelets 238 11.5 Lattice Structures 242 12 Filter Banks 245 12.1 Decomposition and Reconstruction 245 12.2 Analyzing the Elements of the Polyphase Network 247 12.3 Determining the Inverse Functions 248 12.4 Banks of Pseudo-QMF Filters 249 12.5 Determining the Coefficients of the Prototype Filter 253 12.6 Realizing a Bank of Real Filters 254 13 Signal Analysis and Modeling 259 13.1 Autocorrelation and Intercorrelation 259 13.2 Correlogram Spectral Analysis 261 13.3 Single-Frequency Estimation 262 13.4 Correlation Matrix 264 13.5 Modeling 266 13.6 Linear Prediction 268 13.7 Predictor Structures 270 13.8 Multiple Sources - MIMO 273 13.9 Conclusion 275 14 Adaptive Filtering 279 14.1 Principle of Adaptive Filtering 279 14.2 Convergence Conditions 282 14.3 Time Constant 284 14.4 Residual Error 285 14.5 Complexity Parameters 286 14.6 Normalized Algorithms and Sign Algorithms 288 14.7 Adaptive FIR Filtering in Cascade Form 289 14.8 Adaptive IIR Filtering 291 14.9 Conclusion 293 15 Neural Networks 297 15.1 Classification 297 15.2 Multilayer Perceptron 299 15.3 The Backpropagation Algorithm 300 15.4 Examples of Application 303 15.5 Convolution Neural Networks 306 15.6 Recurrent/Recursive Neural Networks 307 15.7 Neural Network and Signal Processing 308 15.8 On Activation Functions 309 15.9 Conclusion 310 16 Error-Correcting Codes 313 16.1 Reed-Solomon Codes 313 16.2 Convolutional Codes 319 16.3 Conclusion 331 17 Applications 335 17.1 Frequency Detection 335 17.2 Phase-locked Loop 337 17.3 Differential Coding of Speech 338 17.4 Coding of Sound 339 17.5 Echo Cancelation 340 17.6 Television Image Processing 342 17.7 Multicarrier Transmission - OFDM 344 17.8 Mobile Radiocommunications 347 References 349 Exercises: Solutions and Hints 351 Index 363