Allen B. Downey - Think DSP: Digital Signal Processing in Python
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- Book:Think DSP: Digital Signal Processing in Python
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If you understand basic mathematics and know how to program with Python, youre 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 theyre applied in the real world. In the first chapter alone, youll 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.
Youll 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.
Allen B. Downey: author's other books
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by Allen B. Downey
Copyright 2016 Allen B. Downey. All rights reserved.
Printed in the United States of America.
Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.
OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://safaribooksonline.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .
- Editors: Nan Barber and Susan Conant
- Production Editor: Kristen Brown
- Copyeditor: Kim Cofer
- Proofreader: Rachel Head
- Indexer: Allen B. Downey
- Interior Designer: David Futato
- Cover Designer: Karen Montgomery
- Illustrator: Rebecca Demarest
- July 2016: First Edition
- 2016-07-11: First Release
See http://oreilly.com/catalog/errata.csp?isbn=9781491938454 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. Think DSP, the cover image of a smooth-billed ani, and related trade dress are trademarks of OReilly Media, Inc.
While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.
Think DSP is available under the Creative Commons Attribution-NonCommercial 3.0 Unported License. The author maintains an online version at http://greenteapress.com/wp/think-dsp/.
978-1-491-93845-4
[LSI]
Signal processing is one of my favorite topics. It is useful in many areas of science and engineering, and if you understand the fundamental ideas, it provides insight into many things we see in the world, and especially the things we hear.
But unless youve studied electrical or mechanical engineering, you probably havent had a chance to learn about signal processing. The problem is that most books (and the classes that use them) present the material bottom-up, starting with mathematical abstractions like phasors. And they tend to be theoretical, with few applications and little apparent relevance.
The premise of this book is that if you know how to program, you can use that skill to learn other things, and have fun doing it.
With a programming-based approach, I can present the most important ideas right away. By the end of the first chapter, youll be able to analyze sound recordings and other signals, and generate new sounds. Each chapter introduces a new technique and an application you can apply to real signals. At each step you learn how to use a technique first, and then how it works.
This approach is more practical and, I hope youll agree, more fun.
The examples and supporting code for this book are in Python. You should know core Python and you should be familiar with object-oriented features, at least using objects if not defining your own.
If you are not already familiar with Python, you might want to start with my other book, Think Python, which is an introduction to Python for people who have never programmed, or Mark Lutzs Learning Python, which might be better for people with programming experience.
I use NumPy and SciPy extensively. If you are familiar with them already, thats great, but I will also explain the functions and data structures I use.
I assume that the reader knows basic mathematics, including complex numbers. You dont need much calculus; if you understand the concepts of integration and differentiation, that will do. I use some linear algebra, but I will explain it as we go along.
The code and sound samples used in this book are available from this GitHub repository: https://github.com/AllenDowney/ThinkDSP. If you are not familiar with Git and GitHub, Git is a version control system that allows you to keep track of the files that make up a project. A collection of files under Gits control is called a repository. GitHub is a hosting service that provides storage for Git repositories and a convenient web interface.
The GitHub home page for my repository provides several ways to work with the code:
You can create a copy of my repository on GitHub by pressing the Fork button. If you dont already have a GitHub account, youll need to create one. After forking, youll have your own repository on GitHub that you can use to keep track of code you write while working on this book. Then you can clone the repository, which means that you copy the files to your computer.
You can clone my repository. You dont need a GitHub account to do this, but you wont be able to write your changes back to GitHub.
If you dont want to use Git at all, you can download the files in a ZIP file using the button in the lower-right corner of the GitHub page.
All of the code is written to work in both Python 2 and Python 3 with no translation.
I developed this book using Anaconda from Continuum Analytics, which is a free Python distribution that includes all the packages youll need to run the code (and lots more). I found Anaconda easy to install. By default it does a user-level installation, not system-level, so you dont need administrative privileges. And it supports both Python 2 and Python 3. You can download Anaconda from http://continuum.io/downloads.
If you dont want to use Anaconda, you will need the following packages:
NumPy for basic numerical computation (http://www.numpy.org)
SciPy for scientific computation (http://www.scipy.org)
matplotlib for visualization (http://matplotlib.org)
Although these are commonly used packages, they are not included with all Python installations, and they can be hard to install in some environments. If you have trouble installing them, I recommend using Anaconda or one of the other Python distributions that include these packages.
Most exercises use Python scripts, but some also use Jupyter notebooks. If you have not used Jupyter before, you can read about it at http://jupyter.org.
There are three ways you can work with the Jupyter notebooks:
Run Jupyter on your computerIf you installed Anaconda, you probably got Jupyter by default. To check, start the server from the command line, like this:
$ jupyter notebookIf its not installed, you can install it in Anaconda like this:
$ conda install jupyterWhen you start the server, it should launch your default web browser or create a new tab in an open browser window.
Run Jupyter on BinderBinder is a service that runs Jupyter in a virtual machine. If you follow the link http://mybinder.org/repo/AllenDowney/ThinkDSP, you should get a Jupyter home page with the notebooks for this book and the supporting data and scripts.
You can run the scripts and modify them to run your own code, but the virtual machine you run in is temporary. Any changes you make will disappear, along with the virtual machine, if you leave it idle for more than about an hour.
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