Sound event detection via dilated convolutional recurrent neural networks

TL;DR

This paper introduces a dilated convolutional recurrent neural network for sound event detection, which expands receptive fields to better capture long-term context, leading to improved performance on benchmark datasets.

Contribution

It proposes a novel dilated CRNN architecture that enhances sound event detection by capturing longer temporal dependencies without increasing model complexity.

Findings

Maximum F1 score increase of 6.3% on TUT Sound Event 2016

Maximum error rate decrease of 4.1% on TUT Sound Event 2016

Demonstrates effectiveness of dilation in improving SED performance

Abstract

Convolutional recurrent neural networks (CRNNs) have achieved state-of-the-art performance for sound event detection (SED). In this paper, we propose to use a dilated CRNN, namely a CRNN with a dilated convolutional kernel, as the classifier for the task of SED. We investigate the effectiveness of dilation operations which provide a CRNN with expanded receptive fields to capture long temporal context without increasing the amount of CRNN's parameters. Compared to the classifier of the baseline CRNN, the classifier of the dilated CRNN obtains a maximum increase of 1.9%, 6.3% and 2.5% at F1 score and a maximum decrease of 1.7%, 4.1% and 3.9% at error rate (ER), on the publicly available audio corpora of the TUT-SED Synthetic 2016, the TUT Sound Event 2016 and the TUT Sound Event 2017, respectively.