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

Understand

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.