Think DSP
Digital Signal Processing in Python
Publisher: O'Reilly Media
Final Release Date: July 2016
Pages: 168

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.

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oreillyThink DSP

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(6 of 6 customers found this review helpful)


Allen Downey does it again!

By Gustavo

from Ann Arbor, MI

About Me Educator

Verified Buyer


  • Concise
  • Easy to understand
  • Helpful examples
  • Well-written


  • Basic
  • Not Scipy Based

Best Uses

  • Novice
  • Student

Comments about oreilly Think DSP:

I bought the book as soon as I saw that it was a new text by Allen Downey, as I am a huge fan of his work. The book proved to be as amazing as I was expecting, explaining clearly the basics of digital signal processing based on demostrating the computational approach and not getting bogged down in mathematical detail. This is a perfect first book to learn signal processing, but it requires prior knowledge of Python (readers without such background can easily acquired with the first of Downey's books: Think Python). The short length of the book means that it can easily incorporated in a Python course, but also means that more advanced readers might not find new information about the subject.
Finally, keep in mind that the book does not teach how to perform signal processing with the SciPy module, because the emphasis is in understanding the algorithms and this is beautifully shown by the author writing his own functions.
I can't wait for the next book of the author's and highly recommend any of his titles.

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