Skip navigation
Title: 

Sound Event Detection in Domestic Environments with Weakly Labeled Data and Soundscape Synthesis

Authors: Turpault, Nicolas
Serizel, Romain
Salamon, Justin
Shah, Ankit Parag
Date Issued: Oct-2019
Citation: N. Turpault, R. Serizel, J. Salamon & A. Shah, "Sound Event Detection in Domestic Environments with Weakly Labeled Data and Soundscape Synthesis", Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019), pages 253–257, New York University, NY, USA, Oct. 2019
Abstract: This paper presents Task 4 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge and provides a first analysis of the challenge results. The task is a follow-up to Task 4 of DCASE 2018, and involves training systems for large-scale detection of sound events using a combination of weakly labeled data, i.e.~training labels without time boundaries, and strongly-labeled synthesized data. The paper introduces Domestic Environment Sound Event Detection (DESED) dataset mixing a part of last year dataset and an additional synthetic, strongly labeled, dataset provided this year that we'll describe more in detail. We also report the performance of the submitted systems on the official evaluation (test) and development sets as well as several additional datasets. The best systems from this year outperform last year's winning system by about 10\% points in terms of F-measure.
First Page: 253
Last Page: 257
DOI: https://doi.org/10.33682/006b-jx26
Type: Article
Appears in Collections:Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019)

Files in This Item:
File SizeFormat 
DCASE2019Workshop_Turpault_44.pdf548.12 kBAdobe PDFView/Open


Items in FDA are protected by copyright, with all rights reserved, unless otherwise indicated.