Digital Signal Processing

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.

Lizhe Tan is a professor in the Department of Electrical and Computer Engineering at Purdue University Northwest. He received his Ph.D. degree in Electrical Engineering from the University of New Mexico, Albuquerque, in 1992. Dr. Tan has extensively taught signals and systems, digital signal processing, analog and digital control systems, and communication systems for many years. He has published a number of refereed technical articles in journals, conference papers and book chapters in the areas of digital signal processing. He has authored and co-authored 4 textbooks, and holds a US patent. Dr. Tan is a senior member of the IEEE and has served as an associate editor for several engineering journals.