LV-49: MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning

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 MAP-Music2Vec, a framework exploring different self-supervised learning algorithmic components and tricks for music audios. Our model achieves comparable results to the state-of-the-art (SOTA) music SSL model Jukebox, despite being significantly lightweight with less than 2% of parameters of the latter. The model will be released on Huggingface.