@conference{903c75b42a01402c92caadb48d6e92a9,
author = " Sharath Adavanne and  Archontis Politis and  Tuomas Virtanen",
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 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.",
booktitle = "Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019)",
month = "10",
pages = "10--14",
title = "{A} {M}ulti-room {R}everberant {D}ataset for {S}ound {E}vent {L}ocalization and {D}etection",
year = "2019",
}