The book gives a comprehensive treatment of modern signal processing theory and its main applications. Its unique perspective combines classic methods based on transforms and filter construction with analytical methods based on explicit signal models, and all algorithms and examples are illustrated with reproducible Matlab code.
The first part of the book deals with classic non-parametric methods based on filters and transforms. A key here is the Discrete Fourier Transform and its relation to the continuous Fourier transform. Further, signals that can be described as stationary stochastic processes are treated, and common methods to estimate their covariance function and spectrum are described.This part ends with a description of different strategies for filtering of signals in the time and frequency domain. Typical application areas in this part are signal conditioning (noise attenuation) and spectral analysis