# auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1712.04382 · HTML: https://mlsanity.com/paper/1712.04382/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2017.

## Built on by (most cited first, 16 of 16)

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- [DeLoRes: Decorrelating Latent Spaces for Low-Resource Audio Representation Learning](https://mlsanity.com/paper/2203.13628/) (2022)
- [A Study on Broadcast Networks for Music Genre Classification](https://mlsanity.com/paper/2208.12086/) (2022)
- [autrainer: A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks](https://mlsanity.com/paper/2412.11943/) (2024)
- [Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs](https://mlsanity.com/paper/2107.13977/) (2021)
- [Interpretable Acoustic Representation Learning on Breathing and Speech Signals for COVID-19 Detection](https://mlsanity.com/paper/2206.13365/) (2022)

## Abstract (arXiv)

auDeep is a Python toolkit for deep unsupervised representation learning from acoustic data. It is based on a recurrent sequence to sequence autoencoder approach which can learn representations of time series data by taking into account their temporal dynamics. We provide an extensive command line interface in addition to a Python API for users and developers, both of which are comprehensively documented and publicly available at https://github.com/auDeep/auDeep. Experimental results indicate that auDeep features are competitive with state-of-the art audio classification.