Title: A Capsule based Approach for Polyphonic Sound Event Detection,
Abstract: Polyphonic sound event detection (polyphonic SED) is an interesting but
challenging task due to the concurrence of multiple sound events. Recently, SED
methods based on convolutional neural networks (CNN) and recurrent neural
networks (RNN) have shown promising performance. Generally, CNN are designed
for local feature extraction while RNN are used to model the temporal
dependency among these local features. Despite their success, it is still
insufficient for existing deep learning techniques to separate individual sound
event from their mixture, largely due to the overlapping characteristic of
features. Motivated by the success of Capsule Networks (CapsNet), we propose a
more suitable capsule based approach for polyphonic SED. Specifically, several
capsule layers are designed to effectively select representative frequency
bands for each individual sound event. The temporal dependency of capsule's
outputs is then modeled by a RNN. And a dynamic threshold method is proposed
for making the final decision based on RNN outputs. Experiments on the TUT-SED
Synthetic 2016 dataset show that the proposed approach obtains an F1-score of
68.8% and an error rate of 0.45, outperforming the previous state-of-the-art
method of 66.4% and 0.48, respectively.