  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.
