Advanced Digital Signal Processing and Noise Reduction
Digital signal processing plays a central role in the development of modern communication and information processing systems. The theory and application of signal processing is concerned with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and therefore noise reduction, the removal of channel distortion, and replacement of lost samples are important parts of a signal processing system. The fourth edition of Advanced Digital Signal Processing and Noise Reduction updates and extends the chapters in the previous edition and includes two new chapters on MIMO systems, Correlation and Eigen analysis and independent component analysis. The wide range of topics covered in this book include Wiener filters, echo cancellation, channel equalisation, spectral estimation, detection and removal of impulsive and transient noise, interpolation of missing data segments, speech enhancement and noise/interference in mobile communication environments. This book provides a coherent and structured presentation of the theory and applications of statistical signal processing and noise reduction methods. * Two new chapters on MIMO systems, correlation and Eigen analysis and independent component analysis* Comprehensive coverage of advanced digital signal processing and noise reduction methods for communication and information processing systems* Examples and applications in signal and information extraction from noisy data* Comprehensive but accessible coverage of signal processing theory including probability models, Bayesian inference, hidden Markov models, adaptive filters and Linear prediction models Advanced Digital Signal Processing and Noise Reduction is an invaluable text for postgraduates, senior undergraduates and researchers in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be of interest to professional engineers in telecommunications and audio and signal processing industries and network planners and implementers in mobile and wireless communication communities.
Why Read This Book
You should read this book if you want a single, application-focused reference that connects statistical DSP theory to practical noise reduction and speech/audio processing techniques. It walks you from stochastic signal modelling through spectral estimation, adaptive filters and modern blind-source methods (ICA, eigenanalysis, MIMO), with examples oriented to real systems.
Who Will Benefit
Advanced undergraduates, graduate students and practicing engineers working on speech/audio enhancement, communications receivers, or noise-robust signal processing who need both theory and practical algorithms.
Level: Advanced — Prerequisites: Undergraduate signals & systems and linear systems theory, basic probability and stochastic processes, linear algebra, and familiarity with basic DSP concepts (DFT/FFT, filtering).
Key Takeaways
- Design optimal linear estimators and Wiener filters for noisy environments.
- Implement and tune adaptive algorithms such as LMS and RLS for real-time noise cancellation.
- Apply parametric and nonparametric spectral estimation and linear prediction (LPC) to speech and audio signals.
- Use eigenanalysis and ICA for blind-source separation and multi-channel noise reduction.
- Understand MIMO signal models and apply multi-channel processing and array techniques to improve SNR.
- Evaluate noise-reduction methods and trade-offs for practical speech enhancement and communication systems.
Topics Covered
- Overview and fundamentals of stochastic signals and processes
- Spectral analysis and nonparametric/parametric methods
- Linear estimation: Wiener filters and signal restoration
- State-space methods and Kalman filtering
- Adaptive filtering: LMS, RLS and convergence issues
- Linear prediction and speech modelling (LPC)
- Noise reduction and speech enhancement techniques
- Correlation, eigenanalysis, PCA and subspace methods
- Independent Component Analysis and blind source separation
- MIMO systems and multi-channel processing
- Applications in audio, speech and communications; case studies
- Implementation notes and performance evaluation
Languages, Platforms & Tools
How It Compares
Covers some of the same application-driven noise-reduction and ICA material as Hyv nen but with broader DSP/statistical foundations; for focused adaptive filter theory see Haykin's "Adaptive Filter Theory", and for in-depth spectral estimation see S. M. Kay's "Modern Spectral Estimation."
