Sample-Level CNN Architectures for Music Auto-Tagging Using Raw Waveforms

Taejun Kim; Jongpil Lee; Juhan Nam

ICASSP 2018accepted0 citations

Sample-Level CNN Architectures for Music Auto-Tagging Using Raw Waveforms

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.

BibTeX

@inproceedings{icassp2018_samplelevelcnnar,
  title = {Sample-Level CNN Architectures for Music Auto-Tagging Using Raw Waveforms},
  author = {Taejun Kim and Jongpil Lee and Juhan Nam},
  booktitle = {ICASSP 2018},
  year = {2018}
}