An implementation example for adopting the BDS data augmentation approach proposed in this paper (paper link will be available soon). The work is presented and published in Interspeech'23. This repository will demonstrate on how to apply BDS using DCASE Challenge-Task4 as example.
This software is a research prototype, solely developed for and published as part of the publication mentioned above. It will neither be maintained nor monitored in any way.
Please refer to the official baseline recipe provided by DCASE website.
Simply adding the BDS function to pytorch lightning trainer step prior to other data augmentation approaches! e.g., inside /local/sed_trainer.py
...
from desed_task.data_augm import BDS
...
def training_step(self, batch, batch_indx):
...
# deriving masks for each dataset
strong_mask = torch.zeros(batch_num).to(features).bool()
weak_mask = torch.zeros(batch_num).to(features).bool()
unlabeled_mask = torch.zeros(batch_num).to(features).bool()
strong_mask[:indx_synth] = 1
weak_mask[indx_synth : indx_weak + indx_synth] = 1
unlabeled_mask[indx_weak + indx_synth:] = 1
# BDS has to apply after some training epochs for reliable pseudo-labeling results
if self.current_epoch/self.hparams["training"]["n_epochs"]>=0.6: # e.g., apply at latest 40% epochs
features = BDS(feats=features,
norm=self.scaler,
scale=self.take_log,
labels_strong=labels,
set_masks=[strong_mask, weak_mask, unlabeled_mask],
model=self.sed_student,
seq_pooling_factor=4,
event_threshold=0.4,
min_frames=40,
bidirectional=False,
stochastic_iter=1)
# other data augmentations, e.g., MixUp, SpecAugment...etc
...If you use this code, please cite the following paper:
Wei-Cheng Lin, Luca Bondi and Shabnam Ghaffarzadegan, "Background Domain Switch: A Novel Data Augmentation Technique for Robust Sound Event Detection", Interspeech 2023.
@InProceedings{LinBDS_2023,
author={W.-C. Lin and L. Bondi and S. Ghaffarzadegan},
title={{Background Domain Switch}: A Novel Data Augmentation Technique for Robust Sound Event Detection},
booktitle={Interspeech 2023},
volume={},
year={2023},
month={August},
pages={XXXX-XXXX},
address = {Dublin, Ireland},
doi={},
}
The code in this repository is open-sourced under the AGPL-3.0 license. See the LICENSE file for details.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent