Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms

Code

• github.com/tae-jun/resemul
  OfficialIn papertf★ 58
• github.com/jaehwlee/tf2-music-tagging-models
  tf★ 0

Abstract

Recent work has shown that the end-to-end approach using convolutional neural network (CNN) is effective in various types of machine learning tasks. For audio signals, the approach takes raw waveforms as input using an 1-D convolution layer. In this paper, we improve the 1-D CNN architecture for music auto-tagging by adopting building blocks from state-of-the-art image classification models, ResNets and SENets, and adding multi-level feature aggregation to it. We compare different combinations of the modules in building CNN architectures. The results show that they achieve significant improvements over previous state-of-the-art models on the MagnaTagATune dataset and comparable results on Million Song Dataset. Furthermore, we analyze and visualize our model to show how the 1-D CNN operates.

Tasks

Reproductions