# A multi-room reverberant dataset for sound event localization and detection Source: [A multi-room reverberant dataset for sound event localization and detection]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:34:37.515Z Authors: Sharath Adavanne, Archontis Politis, Tuomas Virtanen Published: 2019-05-21T11:05:53.000Z ## Abstract 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 with 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Sound Event Localization and Detection on TAU Spatial DCASE Task 3 2019 (full evaluation)](): Sound Event Localization and Detection; TAU Spatial DCASE Task 3 2019 (full evaluation); F1 Score: 83.1. ## Original sources and implementation [Original paper]() [PDF]()