  Sound Event Localization and Detection (SELD) is a problem related to the
field of machine listening whose objective is to recognize individual sound
events, detect their temporal activity, and estimate their spatial location.
Thanks to the emergence of more hard-labeled audio datasets, deep learning
techniques have become state-of-the-art solutions. The most common ones are
those that implement a convolutional recurrent network (CRNN) having previously
transformed the audio signal into multichannel 2D representation. The
squeeze-excitation technique can be considered as a convolution enhancement
that aims to learn spatial and channel feature maps independently rather than
together as standard convolutions do. This is usually achieved by combining
some global clustering operators, linear operators and a final calibration
between the block input and its learned relationships. This work aims to
improve the accuracy results of the baseline CRNN presented in DCASE 2020 Task
3 by adding residual squeeze-excitation (SE) blocks in the convolutional part
of the CRNN. The followed procedure involves a grid search of the ratio
parameter (used in the linear relationships) of the residual SE block, whereas
the hyperparameters of the network remain the same as in the baseline.
Experiments show that by simply introducing the residual SE blocks, the results
obtained are able to improve the baseline considerably.
