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arXiv · 2212.02508

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

Yizhi Li·Ruibin Yuan·Ge Zhang·Yinghao Ma·Chenghua Lin·Xingran Chen·Anton Ragni·Hanzhi Yin·Zhijie Hu·Haoyu He·Emmanouil Benetos·Norbert Gyenge·Ruibo Liu·Jie Fu
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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(Please refer to: https://huggingface.co/m-a-p/music2vec-v1)

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Yizhi Li, Ruibin Yuan, Ge Zhang, Yinghao Ma, Chenghua Lin, Xingran Chen, Anton Ragni, Hanzhi Yin, Zhijie Hu, Haoyu He, Emmanouil Benetos, Norbert Gyenge, Ruibo Liu, Jie Fu. 2022-12-05. MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning. https://arxiv.org/abs/2212.02508

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