Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms
This work addresses music auto-tagging for audio analysis applications, representing an incremental improvement by adapting existing image classification techniques to audio signals.
The paper tackled music auto-tagging by improving 1-D CNN architectures using ResNets, SENets, and multi-level feature aggregation on raw waveforms, achieving significant improvements over previous state-of-the-art models on the MagnaTagATune dataset and comparable results on the Million Song Dataset.
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.