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State-of-the-art sound event detection (SED) methods usually employ a seriesof convolutional neural networks (CNNs) to extract useful features from theinput audio signal, and then recurrent neural networks (RNNs) to model longertemporal context in the extracted features. The number of the channels of theCNNs and size of the weight matrices of the RNNs have a direct effect on thetotal 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 SEDmethod along with the employment of an RNN, introduce implications likeincreased training time, difficulty at gradient flow, and impeding theparallelization of the SED method. To tackle all these problems, we propose thereplacement of the CNNs with depthwise separable convolutions and thereplacement of the RNNs with dilated convolutions. We compare the proposedmethod to a baseline convolutional neural network on a SED task, and achieve areduction of the amount of parameters by 85% and average training time perepoch by 78%, and an increase the average frame-wise F1 score and reduction ofthe average error rate by 4.6% and 3.8%, respectively.

Sound Event Detection with Depthwise Separable and Dilated Convolutions