  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.
