MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised TrainingCCF none

09/2021 – 07/2022

Research Assistant, Supervised by Prof. Richard Stern, Carnegie Mellon University

• Constructed 2-layer learnable front ends in Temporal Modulation Neural Network (TMNN) that combines Mel-like data-driven front ends and temporal modulation filters.
• Examined the proposed front ends surpass state-of-the-art (SOTA) methods on the MagnaTagATune dataset in automatic music tagging, and they are also helpful for keyword spotting on speech commands.
• Analysis of the model performance among tags with different genres and instrument tags.