  Data augmentation methods have shown great importance in diverse supervised
learning problems where labeled data is scarce or costly to obtain. For sound
event localization and detection (SELD) tasks several augmentation methods have
been proposed, with most borrowing ideas from other domains such as images,
speech, or monophonic audio. However, only a few exploit the spatial properties
of a full 3D audio scene. We propose Spatial Mixup, as an application of
parametric spatial audio effects for data augmentation, which modifies the
directional properties of a multi-channel spatial audio signal encoded in the
ambisonics domain. Similarly to beamforming, these modifications enhance or
suppress signals arriving from certain directions, although the effect is less
pronounced. Therefore enabling deep learning models to achieve invariance to
small spatial perturbations. The method is evaluated with experiments in the
DCASE 2021 Task 3 dataset, where spatial mixup increases performance over a
non-augmented baseline, and compares to other well known augmentation methods.
Furthermore, combining spatial mixup with other methods greatly improves
performance.
