Source Separation for Sound Event Detection in domestic environments using jointly trained models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU146145" target="_blank" >RIV/00216305:26230/22:PU146145 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9914755" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9914755</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IWAENC53105.2022.9914755" target="_blank" >10.1109/IWAENC53105.2022.9914755</a>
Alternative languages
Result language
angličtina
Original language name
Source Separation for Sound Event Detection in domestic environments using jointly trained models
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LTAIN19087" target="_blank" >LTAIN19087: Multi-linguality in speech technologies</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of The 17th International Workshop on Acoustic Signal Enhancement (IWAENC 2022)
ISBN
978-1-6654-6867-1
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
1-5
Publisher name
IEEE Signal Processing Society
Place of publication
Bamberg
Event location
Bamberg
Event date
Sep 5, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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