  Artificial sound event detection (SED) has the aim to mimic the human ability
to perceive and understand what is happening in the surroundings. Nowadays,
Deep Learning offers valuable techniques for this goal such as Convolutional
Neural Networks (CNNs). The Capsule Neural Network (CapsNet) architecture has
been recently introduced in the image processing field with the intent to
overcome some of the known limitations of CNNs, specifically regarding the
scarce robustness to affine transformations (i.e., perspective, size,
orientation) and the detection of overlapped images. This motivated the authors
to employ CapsNets to deal with the polyphonic-SED task, in which multiple
sound events occur simultaneously. Specifically, we propose to exploit the
capsule units to represent a set of distinctive properties for each individual
sound event. Capsule units are connected through a so-called "dynamic routing"
that encourages learning part-whole relationships and improves the detection
performance in a polyphonic context. This paper reports extensive evaluations
carried out on three publicly available datasets, showing how the CapsNet-based
algorithm not only outperforms standard CNNs but also allows to achieve the
best results with respect to the state of the art algorithms.
