  Acoustic scene classification (ASC) is one of the most popular problems in
the field of machine listening. The objective of this problem is to classify an
audio clip into one of the predefined scenes using only the audio data. This
problem has considerably progressed over the years in the different editions of
DCASE. It usually has several subtasks that allow to tackle this problem with
different approaches. The subtask presented in this report corresponds to a ASC
problem that is constrained by the complexity of the model as well as having
audio recorded from different devices, known as mismatch devices (real and
simulated). The work presented in this report follows the research line carried
out by the team in previous years. Specifically, a system based on two steps is
proposed: a two-dimensional representation of the audio using the Gamamtone
filter bank and a convolutional neural network using squeeze-excitation
techniques. The presented system outperforms the baseline by about 17
percentage points.
