U-Beat: A Multi-Scale Beat Tracking Model Based on Wave-U-Net

Tian Cheng, Masataka Goto

Abstract: In this paper, we propose a multi-scale model for beat tracking based on the Wave-U-Net model. The proposed model learns multi-scale features by repeatedly resampling feature maps via a series of downsampling blocks and upsampling blocks. With the U-shape structure, we observe that global features are summarized at the bottom blocks. Then, these global features guide feature upsampling for predicting beats with a steady tempo. The local features learned in the downsampling blocks are combined with the upsampled features for predicting beats precisely. Besides the features learned from the waveform, we also combine spectral features at a middle level in the model. Experimental results show that beat tracking performance is improved by combining spectral features.