Digital Signal Processing: System Analysis and Design
This new, fully-revised edition covers all the major topics of digital signal processing (DSP) design and analysis in a single, all-inclusive volume, interweaving theory with real-world examples and design trade-offs. Building on the success of the original, this edition includes new material on random signal processing, a new chapter on spectral estimation, greatly expanded coverage of filter banks and wavelets, and new material on the solution of difference equations. Additional steps in mathematical derivations make them easier to follow, and an important new feature is the do-it-yourself section at the end of each chapter, where readers get hands-on experience of solving practical signal processing problems in a range of MATLAB experiments. With 120 worked examples, 20 case studies, and almost 400 homework exercises, the book is essential reading for anyone taking DSP courses. Its unique blend of theory and real-world practical examples also makes it an ideal reference for practitioners.
Why Read This Book
You will get a single, hands-on reference that blends DSP theory with practical design trade-offs and a large set of MATLAB experiments so you can move from analysis to implementation. The revised edition adds modern coverage of random signal processing, spectral estimation, and greatly expanded filter-bank and wavelet material, making it ideal for real-world audio, radar and communications problems.
Who Will Benefit
Intermediate-level engineers, graduate students, and practitioners who need a practical yet rigorous guide to design and implement DSP algorithms for audio/speech, radar, and communications.
Level: Intermediate — Prerequisites: Undergraduate calculus and linear algebra, basic signals & systems (discrete-time signals, linear systems), introductory probability and random processes, and familiarity with MATLAB (or Octave).
Key Takeaways
- Design and analyze FIR and IIR digital filters and evaluate implementation trade-offs (stability, quantization, structures).
- Apply FFT and spectral-analysis techniques plus modern spectral-estimation methods to analyze signals and noise.
- Implement multirate systems, filter banks and wavelet transforms for subband processing and compression.
- Develop and tune adaptive filters (LMS/RLS and variants) for noise cancellation, echo control, and channel equalization.
- Model and process random signals using statistical signal-processing tools for detection, estimation and system performance analysis.
- Translate theory into practice using MATLAB experiments and case studies to validate algorithms for audio, speech, radar and communications.
Topics Covered
- Introduction and review of discrete-time signals and systems
- z-Transform, difference equations and system functions
- Digital filter structures and implementation issues
- Design of FIR and IIR filters
- Fast Fourier Transform and DFT-based processing
- Spectral analysis and modern spectral-estimation techniques
- Random signals and statistical signal processing
- Adaptive filtering: LMS, RLS and practical variants
- Multirate signal processing and decimation/interpolation
- Filter banks and wavelets (expanded coverage)
- Applications: audio/speech processing, radar and communications examples
- MATLAB experiments, worked examples and case studies
Languages, Platforms & Tools
How It Compares
Compared with Oppenheim & Schafer's Discrete-Time Signal Processing and Proakis & Manolakis's DSP texts, Diniz is more application-oriented with extensive MATLAB 'do-it-yourself' labs, stronger coverage of filter banks/wavelets and added spectral-estimation and random-signal material.
