# Deep Learning Tools for Audacity: Helping Researchers Expand the Artist's Toolkit

arXiv: https://arxiv.org/abs/2110.13323 · HTML: https://mlsanity.com/paper/2110.13323/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [Music Source Separation in the Waveform Domain](https://mlsanity.com/paper/1911.13254/) (2019)
- [Asteroid: the PyTorch-based audio source separation toolkit for researchers](https://mlsanity.com/paper/2005.04132/) (2020)
- [AI Song Contest: Human-AI Co-Creation in Songwriting](https://mlsanity.com/paper/2010.05388/) (2020)

## Built on by (most cited first, 1 of 1)

- [Local deployment of large-scale music AI models on commodity hardware](https://mlsanity.com/paper/2411.09625/) (2024)

## Abstract (arXiv)

We present a software framework that integrates neural networks into the popular open-source audio editing software, Audacity, with a minimal amount of developer effort. In this paper, we showcase some example use cases for both end-users and neural network developers. We hope that this work fosters a new level of interactivity between deep learning practitioners and end-users.