Why Read This Book

You will get a hands-on, MATLAB-driven tour of modern DSP methods that links theory to working code and real applications. The book emphasizes practical algorithm implementation and applied examples in audio/speech, radar, and communications so you can quickly prototype and test DSP ideas in MATLAB.

Who Will Benefit

Engineers and graduate students with basic signals-and-systems background who want to implement and apply DSP algorithms in MATLAB for audio, radar, and communications tasks.

Level: Intermediate — Prerequisites: Basic calculus and linear algebra, introductory signals and systems (continuous/discrete-time concepts), and basic familiarity with MATLAB scripting; prior DSP theory is helpful but not strictly required.

Key Takeaways

• Implement core DSP algorithms in MATLAB, including DFT/FFT-based processing and spectral estimation
• Design and validate FIR and IIR digital filters using windowing, equiripple, and bilinear-transform methods
• Apply adaptive filtering (LMS/RLS) and statistical signal-processing techniques for noise cancellation and parameter estimation
• Use wavelet and time–frequency methods to analyze nonstationary signals and build multirate filter-bank solutions
• Develop application-specific processing chains for audio/speech, radar, and communication signals and test them with MATLAB examples
• Analyze algorithm performance numerically and visually using MATLAB toolboxes and diagnostic plots

Topics Covered

1. 1. Introduction and MATLAB Primer for DSP
2. 2. Signals, Systems, and Discrete-Time Models
3. 3. Sampling, Quantization, and Practical Implementation Issues
4. 4. The Discrete Fourier Transform and FFT Algorithms
5. 5. Digital Filter Design: FIR Techniques
6. 6. Digital Filter Design: IIR Techniques and Realization
7. 7. Spectral Analysis and Power Spectral Density Estimation
8. 8. Adaptive Filtering: LMS, RLS and Applications
9. 9. Multirate Processing and Filter Banks
10. 10. Wavelets and Time–Frequency Methods
11. 11. Statistical Signal Processing and Parameter Estimation
12. 12. Applications: Audio and Speech Processing
13. 13. Applications: Radar and Communications Signal Processing
14. 14. MATLAB Toolboxes, Implementation Tips, and Case Studies
15. Appendices: MATLAB Functions, Sample Code and Datasets

Languages, Platforms & Tools

How It Compares

More application- and MATLAB-focused than Oppenheim & Schafer's Discrete-Time Signal Processing, and broader in MATLAB examples and practical implementation than Richard Lyons' Understanding Digital Signal Processing.