Digital Signal Processing with Kernel Methods (IEEE Press)
A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems
Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research.
Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM
• Presents the necessary basic ideas from both digital signal processing and machine learning concepts
• Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing
• Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing
An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.
Why Read This Book
You will learn how to bridge classical DSP and modern kernel-based machine learning so you can build more flexible, nonlinear signal processing systems for audio, radar, communications and biomedical applications. The book emphasizes practical algorithms, RKHS theory tailored to time-series, and worked examples that make it straightforward to prototype kernel DSP solutions.
Who Will Benefit
Graduate students, research engineers, and experienced DSP/ML practitioners who want to apply kernel methods to audio, speech, radar, and communication signal problems.
Level: Advanced — Prerequisites: Undergraduate linear algebra and probability, basic DSP (sampling, filtering, Fourier analysis), familiarity with statistical learning concepts (regression/classification), and experience coding in MATLAB or Python.
Key Takeaways
- Apply reproducing kernel Hilbert space (RKHS) concepts to time-series and spectral analysis tasks.
- Design and implement kernel-based adaptive filters and nonlinear prediction algorithms.
- Integrate kernel methods with FFT, wavelet transforms, and feature extraction for audio and speech processing.
- Develop kernelized algorithms for radar and communications problems including detection, estimation, and channel modeling.
- Evaluate and regularize kernel DSP methods using statistical performance metrics and cross-validation.
- Prototype kernel DSP systems using common toolkits and translate theory into practical implementations.
Topics Covered
- Introduction: motivations and overview of kernel-DSP integration
- Mathematical foundations: RKHS, kernels, and representer theorems
- Review of classical DSP: sampling, Fourier analysis, filters, and spectral estimation
- Kernel regression and classification for signals
- Kernel spectral analysis, periodograms and FFT-related methods
- Wavelets, time–frequency kernels, and multiresolution representations
- Kernel-based adaptive filtering and online learning
- Statistical signal processing in RKHS: estimation and detection
- Applications to audio and speech processing
- Applications to radar signal processing and target detection
- Applications to communications systems: channel estimation, equalization, and interference mitigation
- Practical implementation issues, software examples and case studies
- Conclusions, open problems and research directions
- Appendices: mathematical tools and code pointers
Languages, Platforms & Tools
How It Compares
Compared with Shawe‑Taylor & Cristianini's Kernel Methods for Pattern Analysis, this book focuses specifically on time‑series, spectral and DSP applications rather than general pattern recognition; compared with classical DSP texts like Haykin's Adaptive Filter Theory it extends adaptive filtering into RKHS and kernel algorithms rather than limiting to linear adaptive filters.
