Digital Signal Processing: Fundamentals and Applications
Digital Signal Processing, Second Edition enables electrical engineers and technicians in the fields of biomedical, computer, and electronics engineering to master the essential fundamentals of DSP principles and practice. Many instructive worked examples are used to illustrate the material, and the use of mathematics is minimized for easier grasp of concepts. As such, this title is also useful to undergraduates in electrical engineering, and as a reference for science students and practicing engineers.
The book goes beyond DSP theory, to show implementation of algorithms in hardware and software. Additional topics covered include adaptive filtering with noise reduction and echo cancellations, speech compression, signal sampling, digital filter realizations, filter design, multimedia applications, over-sampling, etc. More advanced topics are also covered, such as adaptive filters, speech compression such as PCM, u-law, ADPCM, and multi-rate DSP and over-sampling ADC.
New to this edition:
- MATLAB projects dealing with practical applications added throughout the book
- New chapter (chapter 13) covering sub-band coding and wavelet transforms, methods that have become popular in the DSP field
- New applications included in many chapters, including applications of DFT to seismic signals, electrocardiography data, and vibration signals
- All real-time C programs revised for the TMS320C6713 DSK
- Covers DSP principles with emphasis on communications and control applications
- Chapter objectives, worked examples, and end-of-chapter exercises aid the reader in grasping key concepts and solving related problems
- Website with MATLAB programs for simulation and C programs for real-time DSP
Why Read This Book
You should read this book if you want a hands-on, accessible introduction to DSP that keeps the math at a practical level and emphasizes worked examples, MATLAB-based exercises, and real implementation issues. It takes you from core theory (sampling, z-transform, FFT) through filter design and adaptive algorithms to practical considerations like fixed-point implementation and speech/audio applications.
Who Will Benefit
Undergraduate students, practicing electrical engineers, and technicians who need a practical, example-rich grounding in DSP and guidance on taking algorithms into software or hardware implementations.
Level: Intermediate — Prerequisites: Basic calculus and linear algebra, elementary signals-and-systems concepts (continuous vs. discrete signals, basic transforms), and familiarity with programming (MATLAB or equivalent) to run examples.
Key Takeaways
- Design FIR filters using window methods and understand linear-phase realizations
- Design IIR filters using analog prototypes and bilinear transform techniques
- Apply DFT/FFT for spectral analysis and implement efficient FFT algorithms
- Implement adaptive filters (LMS/NLMS) for noise reduction and echo cancellation
- Translate algorithms to software/hardware considerations including fixed-point implementation and DSP processor deployment
- Apply DSP techniques to speech/audio tasks such as compression and basic coding
Topics Covered
- Introduction to Signals and Systems for DSP
- Sampling and Quantization; ADC/DAC basics
- Discrete-Time Transforms and the z-Transform
- The Discrete Fourier Transform and FFT Algorithms
- Spectral Analysis and Windowing
- FIR Filter Structures and Design Methods
- IIR Filter Structures and Design Methods
- Filter Implementation: Fixed-Point, Structures, and Realization
- Multirate Processing: Decimation, Interpolation, and Filter Banks
- Adaptive Filtering: LMS and Variants, Noise Reduction, Echo Cancellation
- Speech and Audio Processing: Compression and Practical Algorithms
- Practical Implementation: MATLAB Examples, C/Firmware Tips, DSP Processor Considerations
- Appendices: Mathematical Tools and Reference Tables
Languages, Platforms & Tools
How It Compares
Less mathematically rigorous than Oppenheim & Schafer or Proakis (which are theory-heavy), and more implementation-oriented than Oppenheim; broadly comparable to Lyons' 'Understanding Digital Signal Processing' in accessibility, but Tan places greater emphasis on worked examples and implementation issues.
