Think DSP : Digital Signal Processing in Python

If you understand basic mathematics and know how to program with Python, you’re ready to dive into signal processing. While most resources start with theory to teach this complex subject, this practical book introduces techniques by showing you how they’re applied in the real world. In the first chapter alone, you’ll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds. Author Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material. You’ll explore: * Periodic signals and their spectrums * Harmonic structure of simple waveforms * Chirps and other sounds whose spectrum changes over time * Noise signals and natural sources of noise * The autocorrelation function for estimating pitch * The discrete cosine transform (DCT) for compression * The Fast Fourier Transform for spectral analysis * Relating operations in time to filters in the frequency domain * Linear time-invariant (LTI) system theory * Amplitude modulation (AM) used in radio Other books in this series include __Think Stats__ and __Think Bayes__, also by Allen Downey. If you understand basic mathematics and know how to program with Python, you're ready to dive into signal processing. While most resources start with theory to teach this complex subject, this practical book introduces techniques by showing you how they're applied in the real world. In the first chapter alone, you'll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds. Author Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material. You'll explore: Periodic signals and their spectrums, Harmonic structure of simple waveforms, Chirps and other sounds whose spectrum changes over time, Noise signals and natural sources of noise, The autocorrelation function for estimating pitch, The discrete cosine transform (DCT) for compression, The Fast Fourier Transform for spectral analysis, Relating operations in time to filters in the frequency domain, Linear time-invariant (LTI) system theory, Amplitude modulation (AM) used in radio. -- Provided by publisher Think DSP is an introduction to Digital Signal Processing in Python. The premise of this book (and the other books in the Think X series) is that if you know how to program, you can use that skill to learn other things. The author is writing this book because he thinks the conventional approach to digital signal processing is backward: most books (and the classes that use them) present the material bottom-up, starting with mathematical abstractions like phasors Sounds and signals Harmonics Non-periodic signals Noise Autocorrelation Discrete cosine transform Discrete fourier transform Filtering and convolution Differentiation and integration LTI systems Modulation and sampling.