This new edition connects mathematical theory with reproducible numerical experiments, helping readers understand not only how to compute a result, but how to interpret it. This computation-first introduction develops sampling, convolution, Fourier analysis, spectral estimation, and digital filter design. It extends these foundations to image and multirate processing, stochastic signals, linear estimation, adaptive filters, Kalman filtering, and time-frequency analysis with wavelets.
Using NumPy, SciPy, Matplotlib, and Jupyter notebooks, readers explore how signal-processing ideas become working computations. Examples and exercises illustrate aliasing, spectral leakage, boundary effects, numerical conditioning, and model assumptions. The emphasis throughout is on building practical Python skills together with the engineering judgment needed to evaluate computational results. Designed for students, engineers, and researchers, the book assumes basic familiarity with Python, linear algebra, elementary probability, and undergraduate signals and systems.
