Result Details
Source Separation for Sound Event Detection in domestic environments using jointly trained models
Sound Event Detection and Source Separation are closely related
tasks: whereas the first aims to find the time boundaries of acoustic
events inside a recording, the goal of the latter is to isolate each
of the acoustic sources into different signals. This paper presents
a Sound Event Detection system formed by two independently pretrained
blocks for Source Separation and Sound Event Detection.
We propose a joint-training scheme, where both blocks are trained
at the same time, and a two-stage training, where each block trains
while the other one is frozen. In addition, we compare the use of
supervised and unsupervised pre-training for the Separation block,
and two model selection strategies for Sound Event Detection. Our
experiments show that the proposed methods are able to outperform
the baseline systems of the DCASE 2021 Challenge Task 4.
Sound Event Detection, Source Separation, DCASE, DESED
@inproceedings{BUT179869,
author="Diego {de Benito Gorron} and Kateřina {Žmolíková} and Doroteo {Torre Toledano}",
title="Source Separation for Sound Event Detection in domestic environments using jointly trained models",
booktitle="Proceedings of The 17th International Workshop on Acoustic Signal Enhancement (IWAENC 2022)",
year="2022",
pages="1--5",
publisher="IEEE Signal Processing Society",
address="Bamberg",
doi="10.1109/IWAENC53105.2022.9914755",
isbn="978-1-6654-6867-1",
url="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9914755"
}