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

@article{Kim2018SampleLevelCA,
  title={Sample-Level CNN Architectures for Music Auto-Tagging Using Raw Waveforms},
  author={Taejun Kim and Jongpil Lee and Juhan Nam},
  journal={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2018},
  pages={366-370}
}
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… CONTINUE READING

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