论文标题
MAP-MUSIC2VEC：一个简单有效的基线，用于自我监督音乐音频表示学习
MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning
论文作者
论文摘要
深度学习社区目睹了对自我监督学习（SSL）的成倍增长。但是，它仍然没有探索如何以一种自我监督的方式来学习有用的原始音乐波形的框架。在这项工作中，我们设计了Music2Vec，该框架探索了不同的SSL算法组件和音乐录音的技巧。我们的模型与最先进（SOTA）音乐SSL模型自动点唱机的结果相当，尽管少于后者的参数少于2％。该模型将在HuggingFace上发布（请参阅：https：//huggingface.co/m-a-p/music2vec-v1）
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)
