1. A benchmark of state-of-the-art sound event detection systems evaluated on synthetic soundscapes
- Author
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Ronchini, Francesca, Serizel, Romain, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Departement of Electrical Engineering-SCD [Leuven] (ESAT-SCD), Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Experiments presented in this paper were carried out using the Grid5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000)., ANR-18-CE23-0020,LEAUDS,Apprentissage statistique pour la compréhension de scènes audio(2018), and European Project: 826276,CPS4EU(2019)
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Sound (cs.SD) ,open-source datasets ,deep learning ,Computer Science - Sound ,Machine Learning (cs.LG) ,Sound event detection ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,Audio and Speech Processing (eess.AS) ,[INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD] ,FOS: Electrical engineering, electronic engineering, information engineering ,Electrical Engineering and Systems Science - Audio and Speech Processing ,synthetic soundscapes - Abstract
International audience; This paper proposes a benchmark of submissions to Detection and Classification Acoustic Scene and Events 2021 Challenge (DCASE) Task 4 representing a sampling of the state-of-the-art in Sound Event Detection task. The submissions are evaluated according to the two polyphonic sound detection score scenarios proposed for the DCASE 2021 Challenge Task 4, which allow to make an analysis on whether submissions are designed to perform fine-grained temporal segmentation, coarse-grained temporal segmentation, or have been designed to be polyvalent on the scenarios proposed. We study the solutions proposed by participants to analyze their robustness to varying level target to non-target signal-to-noise ratio and to temporal localization of target sound events. A last experiment is proposed in order to study the impact of non-target events on systems outputs. Results show that systems adapted to provide coarse segmentation outputs are more robust to different target to non-target signal-to-noise ratio and, with the help of specific data augmentation methods, they are more robust to time localization of the original event. Results of the last experiment display that systems tend to spuriously predict short events when non-target events are present. This is particularly true for systems that are tailored to have a fine segmentation.
- Published
- 2022
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