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Sound event localisation and detection (SELD) is a problem in the field ofautomatic listening that aims at the temporal detection and localisation(direction of arrival estimation) of sound events within an audio clip, usuallyof long duration. Due to the amount of data present in the datasets related tothis problem, solutions based on deep learning have positioned themselves atthe top of the state of the art. Most solutions are based on 2D representationsof the audio (different spectrograms) that are processed by aconvolutional-recurrent network. The motivation of this submission is to studythe squeeze-excitation technique in the convolutional part of the network andhow it improves the performance of the system. This study is based on the onecarried out by the same team last year. This year, it has been decided to studyhow this technique improves each of the datasets (last year only the MICdataset was studied). This modification shows an improvement in the performanceof the system compared to the baseline using MIC dataset.

TASK3 DCASE2021 Challenge: Sound event localization and detection using squeeze-excitation residual CNNs