Digital Signal Processing: A Computer Science Perspective
Get a working knowledge of digital signal processing for computer science applications
The field of digital signal processing (DSP) is rapidly exploding, yet most books on the subject do not reflect the real world of algorithm development, coding for applications, and software engineering. This important new work fills the gap in the field, providing computer professionals with a comprehensive introduction to those aspects of DSP essential for working on today's cutting-edge applications in speech compression and recognition and modem design. The author walks readers through a variety of advanced topics, clearly demonstrating how even such areas as spectral analysis, adaptive and nonlinear filtering, or communications and speech signal processing can be made readily accessible through clear presentations and a practical hands-on approach. In a light, reader-friendly style, Digital Signal Processing: A Computer Science Perspective provides:
* A unified treatment of the theory and practice of DSP at a level sufficient for exploring the contemporary professional literature
* Thorough coverage of the fundamental algorithms and structures needed for designing and coding DSP applications in a high level language
* Detailed explanations of the principles of digital signal processors that will allow readers to investigate assembly languages of specific processors
* A review of special algorithms used in several important areas of DSP, including speech compression/recognition and digital communications
* More than 200 illustrations as well as an appendix containing the essential mathematical background
Why Read This Book
You will get a software-first introduction to DSP that emphasizes algorithm development, implementation issues, and real-world applications such as speech coding and modem algorithms. The book translates core DSP ideas into implementable code patterns and engineering trade-offs, making it useful when you need to move from theory to working systems.
Who Will Benefit
Software engineers, computer scientists, and applied DSP practitioners who need to implement and optimize DSP algorithms for speech, communications, and general signal-analysis applications.
Level: Intermediate — Prerequisites: Basic calculus and linear algebra, introductory signals-and-systems concepts (sampling, convolution), and familiarity with a programming language such as C or MATLAB.
Key Takeaways
- Implement common DSP building blocks (FFT, convolution, filtering) efficiently in software.
- Apply spectral analysis and FFT-based techniques to analyze and process signals.
- Design and implement FIR and IIR digital filters with attention to numerical/implementation issues.
- Implement and evaluate adaptive and nonlinear filtering algorithms in practical settings.
- Apply DSP methods to speech and modem-related problems (basic codecs, feature extraction, spectral techniques).
- Adopt software-engineering practices and performance optimization strategies for DSP code.
Topics Covered
- 1. Introduction: DSP from a Computer Science Viewpoint
- 2. Discrete-Time Signals, Sampling, and Aliasing
- 3. Linear Time-Invariant Systems and Convolution
- 4. Z-Transform and System Analysis
- 5. The Discrete Fourier Transform and FFT Algorithms
- 6. Spectral Analysis and Windowing Techniques
- 7. FIR and IIR Filter Design and Implementation
- 8. Numerical and Fixed-Point Implementation Issues
- 9. Adaptive Filtering and Least-Mean-Squares (LMS) Methods
- 10. Nonlinear Filtering and Practical Algorithms
- 11. DSP for Communications: Modems and Channel Processing
- 12. Speech Signal Processing: Feature Extraction and Compression
- 13. Software Engineering for DSP: Testing, Optimization, and Libraries
- 14. Case Studies and Application Examples
Languages, Platforms & Tools
How It Compares
Less mathematically rigorous than Oppenheim & Schafer or Proakis, but more software- and implementation-focused—closer in spirit to Smith's The Scientist and Engineer's Guide to DSP while covering more communication/speech applications.
