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Sound Event Localization and Detection refers to the problem of identifyingthe presence of independent or temporally-overlapped sound sources, correctlyidentifying to which sound class it belongs, estimating their spatialdirections while they are active. In the last years, neural networks havebecome 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 ofAcoustic Scenes and Events 2020 Challenge Task 3. The algorithm consists of aconvolutional recurrent neural network using rectangular filters, specializedin recognizing significant spectral features related to the task. In order tofurther improve the score and to generalize the system performance to unseendata, the training dataset size has been increased using data augmentation. Thetechnique used for that is based on channel rotations and reflection on the xyplane in the First Order Ambisonic domain, which allows improving Direction ofArrival labels keeping the physical relationships between channels. Evaluationresults on the development dataset show that the proposed system outperformsthe baseline results, considerably improving Error Rate and F-score forlocation-aware detection.

Sound event localization and detection based on crnn using rectangular filters and channel rotation data augmentation