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Digital Signal Processing,

Edition 4 Fundamentals, Applications, and Deep Learning

Editors: By Li Tan, Ph.D., Electrical Engineering, University of New Mexico and Jean Jiang, Ph.D., Electrical Engineering, University of New Mexico

Publication Date: 23 May 2025

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Digital Signal Processing: Fundamentals, Applications, and Deep Learning, Fourth Edition introduces students to the fundamental principles of digital signal processing (DSP) while also providing a working knowledge that they take with them into their engineering careers. Many instructive, worked examples are used to illustrate the material, and the use of mathematics is minimized for an easier grasp of concepts. As such, this title is also useful as a reference for non-engineering students and practicing engineers.

This book goes beyond DSP theory, showing the implementation of algorithms in hardware and software. Additional topics covered include DSP for artificial intelligence, adaptive filtering with noise reduction and echo cancellations, speech compression, signal sampling, digital filter realizations, filter design, multimedia applications, over-sampling, etc. More advanced topics are also covered, such as adaptive filters, speech compression such as pulse-code modulation, µ-law, adaptive differential pulse-code modulation, multi-rate DSP, oversampling analog-to-digital conversion, sub-band coding, wavelet transform, and neural networks.

Key Features

Covers DSP principles with various examples of real-world DSP applications on noise cancellation, communications, control applications, and artificial intelligence
Includes application examples using DSP techniques for deep learning neural networks to solve real-world problems
Provides a new chapter to cover principles of artificial neural networks and convolution neural networks with back-propagation algorithms
Provides hands-on practice, with MATLAB code for worked examples and C programs for real-time DSP for students at https://www.elsevier.com/books-and-journals/book-companion/9780443273353
Offers teaching support, including an image bank, full solutions manual, and MATLAB projects for qualified instructors, available for request at https://educate.elsevier.com/9780443273353

About the author

By Li Tan, Ph.D., Electrical Engineering, University of New Mexico, Professor, Electrical Engineering, Purdue University Northwest, IN, USA and Jean Jiang, Ph.D., Electrical Engineering, University of New Mexico, Engineering Technology, Purdue University Northwest, IN, USA

1. Introduction to Digital Signal Processing
2. Signal Sampling and Quantization
3. Digital Signals and Systems
4. Discrete Fourier Transform and Signal Spectra
5. The z-Transform
6. Digital Signal Processing Systems, Basic Filtering Types, and Digital Filter Realizations
7. Finite Impulse Response Filter Design
8. Infinite Impulse Response Filter Design
9. Adaptive Filters and Applications
10. Waveform Quantization and Compression
11. Multirate Digital Signal Processing, Oversampling of Analog-to-Digital Conversion, and Undersampling of Bandpass Signals
12. Subband and Wavelet-Based Coding
13. Image Processing Basics
14. Digital Signal Processing for Artificial Intelligence
15. Hardware and Software for Digital Signal Processors

Appendix
A: Introduction to the MATLAB Environment
B: Review of Analog Signal Processing Basics
C: Normalized Butterworth and Chebyshev Functions
D: Sinusoidal Steady-State Response of Digital Filters
E: Filter Design Equations by Frequency Sampling Design Method
F: Wavelet Analysis and Synthesis Equations
G: Review of Discrete-Time Random Signals
H: Some Useful Mathematical Formulas Answers to Selected Problems

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