  Acoustic scene classification (ASC) is a problem related to the field of
machine listening whose objective is to classify/tag an audio clip in a
predefined label describing a scene location (e. g. park, airport, etc.). Many
state-of-the-art solutions to ASC incorporate data augmentation techniques and
model ensembles. However, considerable improvements can also be achieved only
by modifying the architecture of convolutional neural networks (CNNs). In this
work we propose two novel squeeze-excitation blocks to improve the accuracy of
a CNN-based ASC framework based on residual learning. The main idea of
squeeze-excitation blocks is to learn spatial and channel-wise feature maps
independently instead of jointly as standard CNNs do. This is usually achieved
by some global grouping operators, linear operators and a final calibration
between the input of the block and its obtained relationships. The behavior of
the block that implements such operators and, therefore, the entire neural
network, can be modified depending on the input to the block, the established
residual configurations and the selected non-linear activations. The analysis
has been carried out using the TAU Urban Acoustic Scenes 2019 dataset
(https://zenodo.org/record/2589280) presented in the 2019 edition of the DCASE
challenge. All configurations discussed in this document exceed the performance
of the baseline proposed by the DCASE organization by 13\% percentage points.
In turn, the novel configurations proposed in this paper outperform the
residual configurations proposed in previous works.
