FreeSound 2019 MixMatch + SpecAugment + Noisy

This repository tries to solve the multi-label freesound classification using MixMatch: A Holistic Approach to Semi-Supervised Learning and SpecAugment.

Dataset is using the freesound - 2019 kaggle competion.

Requirements

Python 3.6+
PyTorch 1.1
torchvision 0.3.0 (older versions are not compatible with this code)
tensorboardX
progress
matplotlib
numpy
librosa

Usage

Train

Train the model by using the freesound 2019 curated and noisy data.

./train.sh

Monitoring training progress

tensorboard --logdir=./result

Results (Accuracy)

0.856 lwlwrap using 5% test set till now.

References

@article{berthelot2019mixmatch,
  title={MixMatch: A Holistic Approach to Semi-Supervised Learning},
  author={Berthelot, David and Carlini, Nicholas and Goodfellow, Ian and Papernot, Nicolas and Oliver, Avital and Raffel, Colin},
  journal={arXiv preprint arXiv:1905.02249},
  year={2019}
}