Sound Event Detection Via Dilated Convolutional Recurrent Neural Networks

Li, Yanxiong; Liu, Mingle; Drossos, Konstantinos; Virtanen, Tuomas
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.

Year:
2020
Book title:
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Series:
IEEE International Conference on Acoustics, Speech and Signal Processing
Pages:
286-290
ISBN:
978-1-5090-6632-2
DOI:
10.1109/ICASSP40776.2020.9054433