# Autoencoding Neural Networks as Musical Audio Synthesizers

arXiv: https://arxiv.org/abs/2004.13172 · HTML: https://mlsanity.com/paper/2004.13172/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

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

- [An investigation of the reconstruction capacity of stacked convolutional autoencoders for log-mel-spectrograms](https://mlsanity.com/paper/2301.07665/) (2023)

## Abstract (arXiv)

A method for musical audio synthesis using autoencoding neural networks is proposed. The autoencoder is trained to compress and reconstruct magnitude short-time Fourier transform frames. The autoencoder produces a spectrogram by activating its smallest hidden layer, and a phase response is calculated using real-time phase gradient heap integration. Taking an inverse short-time Fourier transform produces the audio signal. Our algorithm is light-weight when compared to current state-of-the-art audio-producing machine learning algorithms. We outline our design process, produce metrics, and detail an open-source Python implementation of our model.