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Digital Signal Processing: A Computer-Based Approach, 4th Edition

Digital Signal Processing: A Computer-Based Approach, 4th Edition

Digital signal processing (DSP) is the study of signals and systems that can be represented digitally, such as audio, video, speech, images, and biomedical signals. DSP involves applying mathematical operations and algorithms to these signals to enhance, compress, transform, or analyze them. DSP is widely used in many fields of science, engineering, and technology, such as telecommunications, multimedia, radar, sonar, robotics, biomedicine, and astronomy.

One of the most popular textbooks on DSP is Digital Signal Processing: A Computer-Based Approach, written by Sanjit K. Mitra. This book introduces the tools used in the analysis and design of discrete-time systems for digital signal processing. The revised fourth edition contains a major reorganization of material. Worked-out examples have been included to explain new and difficult concepts and to expose the reader to real-life signal processing problems. MATLAB, Signal Processing Toolbox, and DSP System Toolbox are used to solve numerous application examples. In addition, a set of MATLAB code files is available on a CD bound in the book[^1^].


The book covers topics such as discrete-time signals and systems, frequency-domain analysis of discrete-time systems, sampling and reconstruction of signals, discrete Fourier transform and fast Fourier transform algorithms, design of finite impulse response and infinite impulse response filters, multirate signal processing, adaptive filters, power spectrum estimation, and applications of DSP in speech and audio processing[^1^]. The book also provides review questions, problems, computer experiments, MATLAB projects, and references at the end of each chapter.

Digital Signal Processing: A Computer-Based Approach is suitable for undergraduate courses in electrical engineering, computer engineering, and computer science. It can also be used as a reference book for graduate students and professionals who want to learn more about DSP. The book assumes that the reader has some background in calculus, linear algebra, differential equations, signals and systems, and programming[^1^].DSP has many applications in various domains of science, engineering, and technology. Some of the most common and important applications are:

  • Audio and speech processing: DSP is used to enhance, compress, synthesize, recognize, and analyze audio and speech signals. Examples include noise cancellation, audio coding (such as MP3 and AAC), speech coding (such as GSM and VoIP), speech recognition (such as Siri and Alexa), speech synthesis (such as text-to-speech and voice cloning), and speech enhancement (such as echo cancellation and beamforming).

  • Sonar, radar, and other sensor array processing: DSP is used to process signals from arrays of sensors, such as microphones, hydrophones, antennas, or cameras. Examples include beamforming, direction finding, source localization, target detection, tracking, classification, and imaging.

  • Spectral density estimation: DSP is used to estimate the power spectrum or the frequency content of a signal. Examples include periodogram, Welch method, Bartlett method, Blackman-Tukey method, and parametric methods.

  • Statistical signal processing: DSP is used to apply statistical methods and techniques to signals and systems. Examples include estimation theory, detection theory, hypothesis testing, filtering, prediction, interpolation, smoothing, and machine learning.

  • Digital image processing: DSP is used to manipulate digital images. Examples include image enhancement (such as contrast adjustment and edge detection), image compression (such as JPEG and PNG), image restoration (such as deblurring and inpainting), image segmentation (such as thresholding and region growing), image analysis (such as feature extraction and face recognition), and image synthesis (such as computer graphics and deepfakes).