  Sound Event Localization and Detection refers to the problem of identifying
the presence of independent or temporally-overlapped sound sources, correctly
identifying to which sound class it belongs, estimating their spatial
directions while they are active. In the last years, neural networks have
become the prevailing method for sound Event Localization and Detection task,
with convolutional recurrent neural networks being among the most used systems.
This paper presents a system submitted to the Detection and Classification of
Acoustic Scenes and Events 2020 Challenge Task 3. The algorithm consists of a
convolutional recurrent neural network using rectangular filters, specialized
in recognizing significant spectral features related to the task. In order to
further improve the score and to generalize the system performance to unseen
data, the training dataset size has been increased using data augmentation. The
technique used for that is based on channel rotations and reflection on the xy
plane in the First Order Ambisonic domain, which allows improving Direction of
Arrival labels keeping the physical relationships between channels. Evaluation
results on the development dataset show that the proposed system outperforms
the baseline results, considerably improving Error Rate and F-score for
location-aware detection.
