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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.
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.
168 pages, ebook
Published July 12, 2016
About the author
Allen B. Downey
37 books239 followersAllen Downey is a Professor Emeritus at Olin College and the author of a series of freetextbooks related to software and data science, including Think Python, Think Bayes, and Think Complexity, which are also published by O’Reilly Media. His blog, Probably Overthinking It, features articles on Bayesian probability and statistics. He holds a Ph.D. in computer science from U.C. Berkeley, and M.S. and B.S. degrees from MIT.
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Displaying 1 - 9 of 9 reviews
May 24, 2019
Free online version http://greenteapress.com/wp/think-dsp/
December 30, 2018
This book teaches the fundamentals of DSP, often using audio as the signal. I liked that the book is short (only ~150 pages), fairly cheap, very pragmatic (some theory, but not too much, focused on understanding the basics and being able to put that knowledge to use) and comes with github repo with notebooks with prepared examples from the book. Indeed, for being so short it covers a lot of ground. I found that the topics were introduced in a way that was easy to follow. I really did like the structure of the book. I'm happy with the understanding I got to DSP even without doing the exercises.
Initially I didn't like that the author has made his own ThinkDSP python library but after a while I saw the benefit. The book introduces the library, how to use it, and then the rest of the book benefits from the compact code due to the library.
In short, I recommend the book as an intro to DSP.
Initially I didn't like that the author has made his own ThinkDSP python library but after a while I saw the benefit. The book introduces the library, how to use it, and then the rest of the book benefits from the compact code due to the library.
In short, I recommend the book as an intro to DSP.
October 30, 2019
I enjoyed reading this book, especially the first half of the book, in the second half the book went more into frequency domain analysis where I was actually hoping it was going to remain more applied to sound and music.
Personally I'm not such a big fan of the entire python abstraction layer that was constructed and during my reading I invested some time in diving a bit deeper in the numpy and scipy's myself.
Personally I'm not such a big fan of the entire python abstraction layer that was constructed and during my reading I invested some time in diving a bit deeper in the numpy and scipy's myself.
July 7, 2017
Very pragmatic cookbook for DSP in Python. Without much of theory and full of practical implementations. Can be useful for research work and DSP software development. Don't forget to get Github repository with code examples.
April 20, 2020
Very nice intro to DSP. Explains basic concepts and shows code how to implement them. Easy to follow. Bonus is nicely refactored code which is easy to read. If you want to get quick intro to DSP this is a book for you.
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November 25, 2025What an intro to DSP. basic concepts and shows code how to implement them explaining well. Easy to understand. Bonus is nicely refactored code which is easy to read. If you want to get quick intro to DSP this is a book for you.
October 13, 2018
I was looking for a resource to recap some of the concepts I've leant from the DSP class. This book helped!
August 17, 2024
#nsreads 34
Too proprietary to be much good
Too proprietary to be much good
June 22, 2019
Good for novice signals and systems students.
Displaying 1 - 9 of 9 reviews