  State-of-the-art sound event detection (SED) methods usually employ a series
of convolutional neural networks (CNNs) to extract useful features from the
input audio signal, and then recurrent neural networks (RNNs) to model longer
temporal context in the extracted features. The number of the channels of the
CNNs and size of the weight matrices of the RNNs have a direct effect on the
total amount of parameters of the SED method, which is to a couple of millions.
Additionally, the usually long sequences that are used as an input to an SED
method along with the employment of an RNN, introduce implications like
increased training time, difficulty at gradient flow, and impeding the
parallelization of the SED method. To tackle all these problems, we propose the
replacement of the CNNs with depthwise separable convolutions and the
replacement of the RNNs with dilated convolutions. We compare the proposed
method to a baseline convolutional neural network on a SED task, and achieve a
reduction of the amount of parameters by 85% and average training time per
epoch by 78%, and an increase the average frame-wise F1 score and reduction of
the average error rate by 4.6% and 3.8%, respectively.
