Publication Details

Source Separation for Sound Event Detection in domestic environments using jointly trained models

DE BENITO GORRON, D.; ŽMOLÍKOVÁ, K.; TORRE TOLEDANO, D. Source Separation for Sound Event Detection in domestic environments using jointly trained models. In Proceedings of The 17th International Workshop on Acoustic Signal Enhancement (IWAENC 2022). Bamberg: IEEE Signal Processing Society, 2022. p. 1-5. ISBN: 978-1-6654-6867-1.
Czech title
Oddělení zdroje pro detekci zvukových událostí v domácím prostředí pomocí společně trénovaných modelů
Type
conference paper
Language
English
Authors
de Benito Gorron Diego
Žmolíková Kateřina, Ing., Ph.D. (FIT)
Torre Toledano Doroteo
URL
Keywords

Sound Event Detection, Source Separation, DCASE, DESED

Abstract

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.

Published
2022
Pages
1–5
Proceedings
Proceedings of The 17th International Workshop on Acoustic Signal Enhancement (IWAENC 2022)
ISBN
978-1-6654-6867-1
Publisher
IEEE Signal Processing Society
Place
Bamberg
DOI
UT WoS
000934046400055
EID Scopus
BibTeX
@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"
}
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