  Classification of audio samples is an important part of many auditory
systems. Deep learning models based on the Convolutional and the Recurrent
layers are state-of-the-art in many such tasks. In this paper, we approach
audio classification tasks using capsule networks trained by recently proposed
dynamic routing-by-agreement mechanism. We propose an architecture for capsule
networks fit for audio classification tasks and study the impact of various
parameters on classification accuracy. Further, we suggest modifications for
regularization and multi-label classification. We also develop insights into
the data using capsule outputs and show the utility of the learned network for
transfer learning. We perform experiments on 7 datasets of different domains
and sizes and show significant improvements in performance compared to strong
baseline models. To the best of our knowledge, this is the first detailed study
about the application of capsule networks in the audio domain.

Sound;Machine Learning;Audio and Speech Processing;Machine Learning