Digital Signal Processing and Statistical Classification
Statistical digital signal processing (DSP) has a wide range of applications in the areas of speech, image, video and data for the world of wireless communication, as well as in acoustics, radar, sonar, remote sensing, digital instrumentation and consumer electronics. Covering the fundamentals of this technology, this book provides a technical reference and research tool for practising engineers and for graduate students in both electrical and computer engineering. This resource introduces and integrates advanced digital signal processing and classification together. It also introduces state-of-the-art transforms for digital signal processing and communication applications. It aims to provide professionals with practical guidance in applying DSP knowledge and statistical classification in real-world applications.
Why Read This Book
You should read this book if you need a single reference that ties classical statistical DSP (spectral analysis, estimation, detection) to practical classification techniques used in speech, image, radar and communications. It gives you a hands-on, application-oriented view of transforms and decision rules so you can move from signal models to implementable classifiers and detectors.
Who Will Benefit
Ideal for graduate students and practicing engineers with basic DSP background who want to apply statistical decision theory and transform-based feature extraction to real-world speech, image, radar and communications problems.
Level: Intermediate — Prerequisites: Undergraduate-level signals & systems and probability/stochastic processes; familiarity with basic digital signal processing (Fourier transform, filtering) and linear algebra.
Key Takeaways
- Apply Bayesian and Neyman–Pearson decision rules to detection and classification problems for real signals.
- Derive and implement parameter estimation and spectral-estimation methods for random signals.
- Use transforms (PCA/Karhunen–Loève, DFT, and wavelets) for feature extraction and dimensionality reduction in classification tasks.
- Design and evaluate linear and quadratic classifiers (ML, MAP, LDA) for speech, image and radar applications.
- Integrate adaptive filtering ideas with classification workflows to handle nonstationary signals.
- Translate theory into practice through application examples across communications, acoustics, radar and imaging.
Topics Covered
- 1. Introduction: Scope and Applications of Statistical DSP and Classification
- 2. Review of Signals, Systems and Random Processes
- 3. Fundamentals of Probability, Estimation and Detection Theory
- 4. Spectral Analysis and Parametric/Nonparametric Estimation
- 5. Linear Transforms and Dimensionality Reduction (PCA/KLT, DFT)
- 6. Wavelets and Multiresolution Transforms for Feature Extraction
- 7. Statistical Classification: ML, MAP, LDA and Quadratic Classifiers
- 8. Classifier Performance, ROC Analysis and Model Selection
- 9. Adaptive Methods and Time-Varying Signal Classification
- 10. Applications in Speech, Image, Audio and Radar
- 11. Case Studies and Practical Implementation Issues
- 12. Appendices: Mathematical Background and Algorithmic Recipes
Languages, Platforms & Tools
How It Compares
Covers similar ground to Monson Hayes' "Statistical Digital Signal Processing and Modeling" but places more emphasis on pattern classification and transform-based feature extraction; for a more pattern-recognition-focused treatment, compare with Duda, Hart & Stork's "Pattern Classification" which is less DSP-centric.
