MERT: Acoustic Music Understanding Model with Large-Scale
  Self-supervised Training
Authors: name: Yizhi Li; name: Ruibin Yuan; name: Ge Zhang; name: Yinghao Ma; name: Xingran Chen; name: Hanzhi Yin; name: Chenghua Lin; name: Anton Ragni; name: Emmanouil Benetos; name: Norbert Gyenge; name: Roger Dannenberg; name: Ruibo Liu; name: Wenhu Chen; name: Gus Xia; name: Yemin Shi; name: Wenhao Huang; name: Yike Guo; name: Jie Fu
Published: 2023-05-31 18:27:43

Abstract: Self-supervised learning (SSL) has recently emerged as a promising paradigm
for training generalisable models on large-scale data in the fields of vision,
text, and speech. Although SSL has been proven effective in speech and audio,
its application to music audio has yet to be thoroughly explored. This is
primarily due to the distinctive challenges associated with modelling musical
knowledge, particularly its tonal and pitched characteristics of music. To
address this research gap, we propose an acoustic Music undERstanding model
with large-scale self-supervised Training (MERT), which incorporates teacher
models to provide pseudo labels in the masked language modelling (MLM) style
acoustic pre-training. In our exploration, we identified a superior combination
of teacher models, which outperforms conventional speech and audio approaches
in terms of performance. This combination includes an acoustic teacher based on
Residual Vector Quantization - Variational AutoEncoder (RVQ-VAE) and a musical
teacher based on the Constant-Q Transform (CQT). These teachers effectively
guide our student model, a BERT-style transformer encoder, to better model
music audio. In addition, we introduce an in-batch noise mixture augmentation
to enhance the representation robustness. Furthermore, we explore a wide range
of settings to overcome the instability in acoustic language model
pre-training, which allows our designed paradigm to scale from 95M to 330M
parameters. Experimental results indicate that our model can generalise and
perform well on 14 music understanding tasks and attains state-of-the-art
(SOTA) overall scores. The code and models are online:
https://github.com/yizhilll/MERT.