PySoundTool

PySoundTool is an experimental framework for exploring sound as well as machine learning in the context of sound.

Documentation

For examples and to navigate the code, see the documentation.

About

Note: as is, PySoundTool is not yet a stable framework, meaning changes might periodically be made without extreme focus on backwards compatibility.

Those who might find this useful:

• speech and sound enthusiasts
• digital signal processing / mathematics / physics / acoustics enthusiasts
• deep learning enthusiasts
• researchers
• linguists
• psycholinguists

The main goal of PySoundTool is to provide the code and functionality with more context via visualization, research, and mathematics. Most of the resources used to build the functionality stems from publicly available research and datasets. (For a list of open datasets, see my ever growing collection.)

As it covers quite a large range, from audio file conversion to implementation of trained neural networks, the purpose of PySoundTool is not to be the perfect implementation of all functions (although that is also a goal :P ), but rather a peak into how they can be implemented, hopefully offering others a foundation for trying out different ways of implementation (feature extraction, building neural networks, etc.).

This project is still in the beginning stages and has a lot of room for growth, especially with contributers having a background / knowlege in data science, computer science, machine and deep learning, physics, acoustics, or dsp. Contributers from other backgrounds are also welcome! If you'd like PySoundTool to do something it doesn't, try making it or create an issue.

Installation

Clone this repository. Set the working directory where you clone this repository.

Start a virtual environment:

$ python3 -m venv env

or, to better control your python version:

$ virtualenv -p python3.6 env

Then activate the environment

$ source env/bin/activate
(env)..$

Then install necessary installations via pip:

(env)..$ pip install -r requirements.txt

Examples

You can explore example code via:

• Binder (albeit limited as some packages - librosa, soundfile - cannot be loaded into online environments)
• locally on your machine via Jupyter Notebook
• examples in the documentation.

Binder

Click on the Binder badge at the top of this README (it might take a while to load) and venture into the folder binder_notebooks.

Locally via Jupyter Notebook:

Install and run jupyter notebook:

(env)..$ pip install notebook
(env)..$ jupyter notebook

Venture into the folder jupyter_notebooks and have a go!

Example datasets

If you would like to play around with various types of sound, check out my repo containing mini datasets of sound, ranging from speech to noise. They are very small so don't expect much as it comes to training neural networks. These example datasets are used in some of the documentation examples.

Testing

If you want to run the tests for PySoundTool, they currently use audiofiles available in the example datasets repo, mentioned above. Also, see tests_requirements.txt. The packages located there will need to be installed via:

(env)..$ pip install -r tests_requirements.txt

About the Author

I studied clinical linguistics for my master's which introduced me to the inspiring world of open source software, python programming, and the beauty of acoustics. My interests broadened from academic experimental design to the development of tools for identifiying speech irregularities and beyond. Through this I discovered a bit of a missing niche in the Python community: a high-level sound tool for filtering, analysis, and deep learning that also offers context to its functionality. You can read more about me and my projects on my blog.

The Beginnings of PySoundTool

This project stemmed from the Prototype Fund project NoIze which was presented at PyConDE / PyData Berlin in 2019. This fork broadens the application of the software from smart noise filtering to general sound analysis, filtering, visualization, preparation, etc. Therefore the name has been adapted to more general sound functionality.