  In this paper, we propose a novel data augmentation method for training
neural networks for Direction of Arrival (DOA) estimation. This method focuses
on expanding the representation of the DOA subspace of a dataset. Given some
input data, it applies a transformation to it in order to change its DOA
information and simulate new potentially unseen one. Such transformation, in
general, is a combination of a rotation and a reflection. It is possible to
apply such transformation due to a well-known property of First Order
Ambisonics (FOA). The same transformation is applied also to the labels, in
order to maintain consistency between input data and target labels. Three
methods with different level of generality are proposed for applying this
augmentation principle. Experiments are conducted on two different DOA
networks. Results of both experiments demonstrate the effectiveness of the
novel augmentation strategy by improving the DOA error by around 40%.
