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A Multi-room Reverberant Dataset for Sound Event Localization and Detection

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publicationProceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019)
ISBN (Electronic)978-0-578-59596-2
Publication statusPublished - Oct 2019
Publication typeA4 Article in a conference publication
EventWorkshop on Detection and Classification of Acoustic Scenes and Events - New York, United States
Duration: 25 Oct 201926 Oct 2019


WorkshopWorkshop on Detection and Classification of Acoustic Scenes and Events
Abbreviated titleDCASE
CountryUnited States
CityNew York


This paper presents the sound event localization and detection (SELD) task setup for the DCASE 2019 challenge. The goal of the SELD task is to detect the temporal activities of a known set of sound event classes, and further localize them in space when active. As part of the challenge, a synthesized dataset where each sound event associated with a spatial coordinate represented using azimuth and elevation angles is provided. These sound events are spatialized using real-life impulse responses collected at multiple spatial coordinates in five different rooms with varying dimensions and material properties. A baseline SELD method employing a convolutional recurrent neural network is used to generate benchmark scores for this reverberant dataset. The benchmark scores are obtained using the recommended cross-validation setup.

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