Update (Oct. 12, 2019) Here is my repo called PySoundTool with various tools for visualizing sound, training sound classifiers, etc. Additionally, you can access Jupyter notebooks on mybinder, without needing an account. It may take a few minutes to load as I include a small amount of audio data for you to experiment with. They offer basically the same functionality as the notebooks I describe below on notebooks.ai.

(On to the older post)

I put together a Jupyter Lab notebook for creating and analyzing signals in Python. I try to make the math behind that of signal creation, the Nyquist Theorem, and the fast Fourier Transform (FFT) a little bit more accessible.

Note: this notebook should be more of an aid to additional materials about digital signal processing, especially that of the FFT. My goal is to offer a perspective of some components of the FFT that I found missing when researching it myself; additionally, the Jupyter notebook offers the opportunity to directly see how frequency, noise, sample rate, etc. influence digital signal processing.

Upcoming events:

September 2nd we present our smart noise filter prototype NoIze.

September 10th we will give a workshop for PyLadies Berlin, centering on our open source smart filter.

October 11th we will give a talk at PyCon / PyData Berlin called Take control of your hearing: Accessible methods to build a smart noise filter