  After constructing a deep neural network for urban sound classification, this
work focuses on the sensitive application of assisting drivers suffering from
hearing loss. As such, clear etiology justifying and interpreting model
predictions comprise a strong requirement. To this end, we used two different
representations of audio signals, i.e. Mel and constant-Q spectrograms, while
the decisions made by the deep neural network are explained via layer-wise
relevance propagation. At the same time, frequency content assigned with high
relevance in both feature sets, indicates extremely discriminative information
characterizing the present classification task. Overall, we present an
explainable AI framework for understanding deep urban sound classification.
