Map-music2vec: A simple and effective baseline for self-supervised music audio representation learning

Abstract: The deep learning community has witnessed an exponentially growing interest in self-supervised learning (SSL). However, it still remains unexplored how to build a framework for learning useful representations of raw music waveforms in a self-supervised manner. In this work, we design Music2Vec, a framework exploring different SSL algorithmic components and tricks for music audio recordings. Our model achieves comparable results to the state-of-the-art (SOTA) music SSL model Jukebox, despite being significantly smaller with less than 2% of parameters of the latter. The model will be released on Huggingface.

• Paper link.

• Huggingface checkpoints.

• Accepted by ISMIR 2022, late breaking demo.