R CRNN: Region based Convolutional Recurrent Neural Network for Audio Event Detection
This paper proposes a Region based Convolutional Recurrent Neural Network (R CRNN) for audio event detection (AED) The proposed network is inspired by Faster RCNN, a well known region based convolutional network framework for visual object detection. Different from the original Faster RCNN, a recurrent layer is added on top of the convolutional network to capture the long term temporal context from the extracted high level features. While most of the previous works on AED generate predictions at frame level first, and then use post processing to predict the onset/offset timestamps of events from a probability sequence; the proposed method generates predictions at event level directly and can be trained end to end with a multitask loss, which optimizes the classification and localization of audio events simultaneously. The proposed method is tested on DCASE 2017 Challenge dataset. To the best of our knowledge, R CRNN is the best performing single model method among all methods without using ensembles both on development and evaluation sets. Compared to the other region based network for AED (R FCN) with an event based error rate (ER) of 0.18 on the development set, our method reduced the ER to half.
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