# Unsupervised Feature Learning for Audio Analysis

arXiv: https://arxiv.org/abs/1712.03835 · HTML: https://mlsanity.com/paper/1712.03835/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2017.

## Built on by (most cited first, 3 of 3)

- [Unsupervised Discriminative Learning of Sounds for Audio Event Classification](https://mlsanity.com/paper/2105.09279/) (2021)
- [Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)](https://mlsanity.com/paper/2107.03690/) (2021)
- [Using system context information to complement weakly labeled data](https://mlsanity.com/paper/2107.10236/) (2021)

## Abstract (arXiv)

Identifying acoustic events from a continuously streaming audio source is of interest for many applications including environmental monitoring for basic research. In this scenario neither different event classes are known nor what distinguishes one class from another. Therefore, an unsupervised feature learning method for exploration of audio data is presented in this paper. It incorporates the two following novel contributions: First, an audio frame predictor based on a Convolutional LSTM autoencoder is demonstrated, which is used for unsupervised feature extraction. Second, a training method for autoencoders is presented, which leads to distinct features by amplifying event similarities. In comparison to standard approaches, the features extracted from the audio frame predictor trained with the novel approach show 13 % better results when used with a classifier and 36 % better results when used for clustering.