In this repo we have uploaded the code used for DCASE2020 Track 4 Challenge.
We achieved as a team the 10th place in the SED category.
However, regarding class-wise detection performance we placed first for the cat class, thus this repo can be at least useful for cat lovers 😺.
We employed the challenge official baseline architecture based on CNN-RNN and Mean Teacher training.
This allowed us to focus on the training procedure,
on the feature pre-processing and on the prediction post-processing and smoothing.
Regarding training procedure, we achieved good validation set results by combining the Mean Teacher with Domain Adversarial Training
and online creation of synthetic labeled examples.
This, further combined with Hidden Markov Model prediction smoothing allowed us to achieve 45.2 %event-based macro F1 score
on the validation set. We also explored feature pre-processing by employing several parallel
Per-Channel Energy Normalization front-end layers (PPCEN).
For more information have a look at our DCASE2020 system description and to the official DCASE 2020 Task 4 Challenge results page
If you find this code useful please cite:
@article{CornellDCASE2020,
title={The UNIVPM-INRIA Systems for the DCASE 2020 TASK 4},
author={Samuele Cornell, Giovanni Pepe, Emanuele Principi, Manuel Pariente, Michel Olvera, Leonardo Gabrielli, Stefano Squartini},
year={2020},
journal={DCASE 2020 Task 4 Challenge System description},
}