A Course in Digital Signal Processing
A comprehensive, practical and up-to-date exposition on digital signal processing. Both mathematical and useful, this book uses a rigorous approach to help readers learn the theory and practice of DSP. It discusses practical spectral analysis, including the use of windows for spectral analysis, sinusoidal signal analysis, and the effect of noise. It also covers FIR and IIR filters, including detailed design procedures and MATLAB tools.
Why Read This Book
You should read this book if you want a mathematically rigorous yet practical treatment of core DSP topics—spectral analysis, filter design, and parameter estimation—with worked examples and MATLAB-oriented tools. It bridges theory and engineer-focused practice so you can both understand proofs and apply algorithms to real signals.
Who Will Benefit
Upper-undergraduate and graduate students, signal-processing engineers, and practitioners who need a solid theoretical foundation plus practical techniques for spectral analysis and filter design.
Level: Advanced — Prerequisites: Undergraduate signals & systems and calculus, basic linear algebra, and introductory probability and random processes (familiarity with MATLAB is helpful).
Key Takeaways
- Analyze discrete-time signals using DTFT, DFT and Z-transform formalisms.
- Design and implement FIR and IIR digital filters using standard techniques and evaluate their performance.
- Perform practical spectral analysis with windowing, periodograms and understanding of noise effects.
- Derive and apply parametric spectral estimation and linear prediction (AR/MA/ARMA) methods.
- Use MATLAB to prototype, visualize, and validate DSP algorithms and filter designs.
Topics Covered
- Introduction and Review of Discrete-Time Signals and Systems
- The Discrete-Time Fourier Transform and Properties
- The Z-Transform and Analysis of LTI Systems
- Sampling, DFT, and the Fast Fourier Transform
- FIR Filter Theory and Design Techniques
- IIR Filter Design and Realization Structures
- Practical Issues: Numerical Considerations and Implementation
- Nonparametric Spectral Analysis: Periodogram and Windowing
- Parametric Spectral Estimation and Linear Prediction
- Stochastic Signals and Power Spectral Density
- Estimation Theory and Wiener Filtering (practical aspects)
- MATLAB Examples and Numerical Tools for DSP
Languages, Platforms & Tools
How It Compares
Covers similar foundational ground as Oppenheim & Schafer's Discrete-Time Signal Processing but places more emphasis on practical spectral-analysis techniques and MATLAB tools; for deeper statistical modeling compare to P. D. Welch/Hayes-style texts on statistical DSP.
