We introduce DECAR, a self-supervised pre-training approach for learning general-purpose audio representations. Our system is based on clustering: it utilizes an offline clustering step to provide target labels that act as pseudo-labels for solving a prediction task. We develop on top of recent advances in self-supervised learning for computer vision and design a lightweight, easy-to-use self-supervised pre-training scheme. We pre-train DECAR embeddings on a balanced subset of the large-scale Audioset dataset and transfer those representations to 9 downstream classification tasks, including speech, music, animal sounds, and acoustic scenes. Furthermore, we conduct ablation studies identifying key design choices and also make all our code and pre-trained models publicly available.
@article{arxiv.2110.08895,
title = {DECAR: Deep Clustering for learning general-purpose Audio Representations},
author = {Sreyan Ghosh and Sandesh V Katta and Ashish Seth and S. Umesh},
journal= {arXiv preprint arXiv:2110.08895},
year = {2023}
}