Modern Digital Signal Processing
Roberto Cristi conveys the excitement of the Digital Signal Processing field in which students can experiment with sounds, images, and video. Using a wealth of applications, the book covers Digital Signal Processing material well suited to today's diverse student population. The author presents the material in a logical sequence so that students can appreciate how concepts develop. The book can be effectively used in a university classroom or as a base for self-study.s
Why Read This Book
You should read this book if you want a practical, application-driven introduction to modern DSP that connects theory to real audio, image, radar, and communications problems. You will learn concepts in a logical progression with plenty of experiments and examples so you can move from understanding to implementing DSP algorithms quickly.
Who Will Benefit
Undergraduate or graduate engineering students and practicing engineers (audio, communications, radar) who want a hands-on, application-focused DSP text to learn algorithms and implement them in real systems.
Level: Intermediate — Prerequisites: Single-variable calculus, basic linear algebra and complex numbers, an introductory signals & systems background (continuous/discrete-time), and familiarity with MATLAB or equivalent for running examples (recommended).
Key Takeaways
- Implement time- and frequency-domain DSP algorithms for real signals (audio, speech, radar, communications).
- Design and realize FIR and IIR digital filters using classical and window-based techniques.
- Apply FFT-based methods for spectral analysis, fast convolution, and efficient signal processing.
- Use adaptive filtering (e.g., LMS) and statistical techniques to estimate and track nonstationary signals.
- Apply wavelet and time–frequency methods for transient analysis and multiresolution signal representation.
- Translate theoretical DSP concepts into practical experiments and MATLAB/Octave code for real applications.
Topics Covered
- 1. Introduction and Overview of DSP Applications
- 2. Review of Signals, Systems, and Sampling
- 3. The z-Transform and Discrete-Time Fourier Analysis
- 4. Discrete Fourier Transform and the FFT
- 5. Frequency-Domain Signal Processing and Spectral Analysis
- 6. Windowing and Periodogram Methods
- 7. FIR Filter Design and Implementation
- 8. IIR Filter Design and Realization
- 9. Multirate Signal Processing and Decimation/Interpolation
- 10. Adaptive Filters and LMS Algorithms
- 11. Statistical Signal Processing and Estimation
- 12. Wavelets and Time–Frequency Analysis
- 13. Applications: Audio and Speech Processing
- 14. Applications: Radar and Communications Signal Processing
- 15. Practical Experiments, MATLAB Examples, and Project Ideas
Languages, Platforms & Tools
How It Compares
More application-focused and approachable than Oppenheim & Schafer's Discrete-Time Signal Processing and Proakis' Digital Signal Processing, which are more mathematically exhaustive—Cristi emphasizes experiments and real-world examples.
