Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Detection
This work addresses the challenge of detecting diverse acoustic events over extended time periods, which is incremental as it builds on existing CNN architectures like VGGNet.
The authors tackled acoustic event detection by introducing a deep convolutional neural network with a large input field and a novel data augmentation method, achieving a 16% absolute improvement over state-of-the-art methods.
We propose a novel method for Acoustic Event Detection (AED). In contrast to speech, sounds coming from acoustic events may be produced by a wide variety of sources. Furthermore, distinguishing them often requires analyzing an extended time period due to the lack of a clear sub-word unit. In order to incorporate the long-time frequency structure for AED, we introduce a convolutional neural network (CNN) with a large input field. In contrast to previous works, this enables to train audio event detection end-to-end. Our architecture is inspired by the success of VGGNet and uses small, 3x3 convolutions, but more depth than previous methods in AED. In order to prevent over-fitting and to take full advantage of the modeling capabilities of our network, we further propose a novel data augmentation method to introduce data variation. Experimental results show that our CNN significantly outperforms state of the art methods including Bag of Audio Words (BoAW) and classical CNNs, achieving a 16% absolute improvement.