Sound gives signal processing a concrete starting point. Allen B. Downey's Think DSP connects Python experiments with the mathematics behind waves, letting a signal be generated, heard, inspected, and changed as the concepts develop.
Time, Frequency, and System Behavior
Audio signals, spectra, harmonics, the Fourier transform and FFT, filters, convolution, and linear time-invariant systems form the material. Python provides a way to move between time and frequency representations.
The relationship between those views gives the approach its value. A spectrum reveals information that a waveform alone may not make clear; filtering shows how a system transforms an input. Observable effects provide context for the calculations.
An Experimental Introduction
Readers with some Python experience will find an accessible route into DSP through audio. Further mathematical study still has a place. The book is especially useful for approaching a concept through a small experiment and then following its explanation.
Get the Book
- Green Tea Press: Think DSP
- Amazon: Think DSP: Digital Signal Processing in Python