Digital Signal Processing: Fundamentals, Applications, and Deep Learning

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.

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