# Improving Self-Supervised Learning for Audio Representations by Feature Diversity and Decorrelation

arXiv: https://arxiv.org/abs/2303.03717 · HTML: https://mlsanity.com/paper/2303.03717/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2023.

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

- [On the Effect of Data-Augmentation on Local Embedding Properties in the Contrastive Learning of Music Audio Representations](https://mlsanity.com/paper/2401.08889/) (2024)

## Abstract (arXiv)

Self-supervised learning (SSL) has recently shown remarkable results in closing the gap between supervised and unsupervised learning. The idea is to learn robust features that are invariant to distortions of the input data. Despite its success, this idea can suffer from a collapsing issue where the network produces a constant representation. To this end, we introduce SELFIE, a novel Self-supervised Learning approach for audio representation via Feature Diversity and Decorrelation. SELFIE avoids the collapsing issue by ensuring that the representation (i) maintains a high diversity among embeddings and (ii) decorrelates the dependencies between dimensions. SELFIE is pre-trained on the large-scale AudioSet dataset and its embeddings are validated on nine audio downstream tasks, including speech, music, and sound event recognition. Experimental results show that SELFIE outperforms existing SSL methods in several tasks.