Welcome to the docs!¶
SoundPy is a research based Python package for exploring and experimenting with sound and deep learning.
Those who might find this useful:
speech and sound enthusiasts
digital signal processing / mathematics / physics / acoustics enthusiasts
deep learning enthusiasts
The main goal of SoundPy 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.
As it covers quite a large range, from audio file conversion to implementation of trained neural networks, the purpose of SoundPy 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 people 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 contributors having a background / knowlege in data science, computer science, machine and deep learning, physics, acoustics, or dsp. Contributors from other backgrounds are also welcome! If you’d like SoundPy to do something it doesn’t, try making it or create an issue.
- SoundPy Example Use Cases
- SoundPy Functionality
- Built-In Functionality (non Deep Learning)
- Built-In Functionality (Deep Learning)
- Augment audio data
- Working with audio files
- Organizing datasets
- Working with signals
- Filters: Wiener and Band Spectral Subtraction
- Extract and manipulate audio features
- Template deep neural networks
- Additional model setup (e.g. Early Stopping)
- Feeding large datasets to models
- Other useful non-specific functionality
- Customized Errors