DSP First
For introductory courses (freshman and sophomore courses) in Digital Signal Processing and Signals and Systems. Text may be used before the student has taken a course in circuits.
DSP First and it's accompanying digital assets are the result of more than 20 years of work that originated from, and was guided by, the premise that signal processing is the best starting point for the study of electrical and computer engineering. The "DSP First" approach introduces the use of mathematics as the language for thinking about engineering problems, lays the groundwork for subsequent courses, and gives students hands-on experiences with MATLAB.
The Second Edition features three new chapters on the Fourier Series, Discrete-Time Fourier Transform, and the The Discrete Fourier Transform as well as updated labs, visual demos, an update to the existing chapters, and hundreds of new homework problems and solutions.
Why Read This Book
You will get a fast, intuitive grounding in digital signal processing that emphasizes hands-on learning and real-world applications. DSP First (2nd Ed) teaches core DSP concepts—sampling, FFTs, filter design, spectral analysis, and adaptive methods—through clear explanations and classroom-tested labs so you can apply techniques to audio, speech, radar, and communications problems quickly.
Who Will Benefit
Ideal for freshman/sophomore electrical or computer engineering students and early-career engineers who want a practical, example-driven introduction to DSP before taking advanced circuits or theory-heavy courses.
Level: Beginner — Prerequisites: High-school algebra and basic trigonometry; elementary calculus and basic programming experience (MATLAB/Python helpful) are useful but not required — no prior circuits or DSP background needed.
Key Takeaways
- Explain and work with discrete-time signals, sampling, aliasing, and quantization.
- Compute and interpret spectra using the DFT/FFT and perform practical spectral analysis.
- Design, analyze, and implement FIR and IIR digital filters and windowing methods.
- Apply DSP techniques to practical application areas such as audio/speech, radar, and communications.
- Implement basic adaptive filters and understand foundational ideas in statistical signal processing.
- Run hands-on experiments and simulations using MATLAB/Octave or Python to validate DSP concepts.
Topics Covered
- Introduction: Why DSP First and engineering perspective
- Discrete-Time Signals and Systems
- Sampling, Aliasing, and the A/D Process
- Time-Domain Analysis and Convolution
- Frequency-Domain Concepts and Fourier Series
- The Discrete Fourier Transform and Fast Fourier Transform
- Digital Filters: FIR and IIR Fundamentals
- Filter Design Techniques, Windowing, and Implementation
- Multirate Processing: Decimation and Interpolation
- Spectral Analysis, Time–Frequency Methods, and Wavelets
- Adaptive Filtering and Introductory Statistical Signal Processing
- Applications: Audio and Speech Processing
- Applications: Radar and Communications Examples
- Laboratory Exercises, Software Tools, and Mathematical Appendices
Languages, Platforms & Tools
How It Compares
More introductory and lab-oriented than Oppenheim & Schafer's Discrete-Time Signal Processing, and more structured for freshmen labs than Steve W. Smith or Lyons' Understanding Digital Signal Processing, which are either more advanced or more practitioner-oriented.
