  This report presents the dataset and the evaluation setup of the Sound Event
Localization & Detection (SELD) task for the DCASE 2020 Challenge. The SELD
task refers to the problem of trying to simultaneously classify a known set of
sound event classes, detect their temporal activations, and estimate their
spatial directions or locations while they are active. To train and test SELD
systems, datasets of diverse sound events occurring under realistic acoustic
conditions are needed. Compared to the previous challenge, a significantly more
complex dataset was created for DCASE 2020. The two key differences are a more
diverse range of acoustical conditions, and dynamic conditions, i.e. moving
sources. The spatial sound scenes are created using real room impulse responses
captured in a continuous manner with a slowly moving excitation source. Both
static and moving sound events are synthesized from them. Ambient noise
recorded on location is added to complete the generation of scene recordings. A
baseline SELD method accompanies the dataset, based on a convolutional
recurrent neural network, to provide benchmark scores for the task. The
baseline is an updated version of the one used in the previous challenge, with
input features and training modifications to improve its performance.
