/audio-auto-tagging-models

Tensorflow2 implementation of various music auto-tagging models

Primary LanguagePython

Tensorflow2 implementation of various music auto-tagging models

Evaluation of CNN-based Automatic Music Tagging Models

Minz Won, Andres Ferraro, Dmitry Bogdanov, and Xavier Serra

SMC, 2020

Reference

Available Models

  • Sample-level CNN : Sample-level Deep Convolutional Neural Networks for Music Auto-tagging using Raw Waveforms, Lee et al., 2017 pdf
  • Sample-level CNN + Squeeze-and-excitation : Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms, Kim et al., 2018 arxiv, code
  • Music-SincNet : Music Auto-tagging with Learning Filter Banks, Lee and Shin, 2020 pdf
  • Harmonic-CNN : Data-driven Harmonic filters for Audio Representation Learning, Won et al., 2020 pdf, code

Upcoming Models

  • Self-attention

Usage

  • Requirements

conda env create -n {ENV_NAME} --file environment.yaml
conda activate {ENV_NAME}
  • Preprocessing

python -u preprocess.py run ../dataset
python -u split.py run ../dataset
  • Training

python main.py
  • Options

'--gpu', type=str, default='0'
'--encoder_type', type=str, default='HC', choices=['HC', 'MS', 'SC']
'--block', type=str, default='rese', choices=['basic', 'se', 'res', 'rese']