  Audio event localization and detection (SELD) have been commonly tackled
using multitask models. Such a model usually consists of a multi-label event
classification branch with sigmoid cross-entropy loss for event activity
detection and a regression branch with mean squared error loss for
direction-of-arrival estimation. In this work, we propose a multitask
regression model, in which both (multi-label) event detection and localization
are formulated as regression problems and use the mean squared error loss
homogeneously for model training. We show that the common combination of
heterogeneous loss functions causes the network to underfit the data whereas
the homogeneous mean squared error loss leads to better convergence and
performance. Experiments on the development and validation sets of the DCASE
2020 SELD task demonstrate that the proposed system also outperforms the DCASE
2020 SELD baseline across all the detection and localization metrics, reducing
the overall SELD error (the combined metric) by approximately 10% absolute.
