Digital Signal Processing: Fundamentals, Applications, and Deep Learning, Fourth Edition introduces students to the fundamental principles of DSP while also providing a working knowledge they can 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 book is also useful as a reference for non-engineering students and practicing engineers. The 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 PCM, µ-law, ADPCM, and multi-rate DSP, over-sampling ADC subband coding, and wavelet transform.

Key Features

• Covers DSP principles with an emphasis on communications and control applications
• Includes chapter objectives, worked examples, and end-of-chapter exercises that aid the reader in grasping key concepts and solving related problems
• Offers a robust set of ancillary materials for instructors and students, including a solutions manual, sample test, MATLAB projects, image bank, MATLAB code for worked examples, and C programs for real-time DSP

New Features

• Includes a new chapter on digital signal processing for artificial intelligence and deep learning, featuring deep learning tools from both MATLAB and Python