This directory contains Python implementations of the exercises in Eric Tarr's Hack Audio: An Introduction to Computer Programming and Digital Signal Processing in Matlab.
Why killaudio? The name Hack Audio reminded me of the comic book series Kill Audio by Coheed and Cambria's frontman, Claudio Sanchez.
This project uses Anaconda for dependency and environment management. I use a MacBook Pro as my development machine, and I primarily work on the command line.
The instructions below should work largely as-written on a Linux machine, but if you're using Windows, you'll either want to consider using the Windows Subsystem for Linux (so you can follow along using the same instructions below), or will need to spend some time investigating getting everything up and running for a Windows environment.
First, install Anaconda.
- If you're using a Mac, follow these command line installer instructions.
- If you're running Linux, follow these instructions.
Next, we're going to create a dedicated environment for the dependencies we need. The official documentation for doing so is here.
In the example below, I've chosen to name the environment killaudio.
$ conda create -n killaudio
$ conda activate killaudio
We need the following dependencies for the examples in this repository.
$ conda install numpy
$ conda install scipy
$ conda install matplotlib
$ conda install jupyterlab
In some directories, you'll find files ending with the .ipynb
extension. These are interactive Python notebook files, which allow you to write Python code and execute it in realtime inside of the notebook, along formatted documentation, etc.
In the course of creating these examples, I'm creating notebooks in Google Colab, which is a free to use, in-browser environment for interactive Python notebooks (using Google's computing resources).
If you clone this repository and want to open the files in Jupyter Notebooks instead of Google Colab, you can run the following:
$ jupyter-lab <filename>
If at any point you need to list out the environments on your development machine, you can run:
$ conda info --envs
And to apply a given environment, run:
$ conda activate <envname>