2016

Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Detection

Takahashi, Naoya, Gygli, Michael, Pfister, Beat et al.

Understand

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.

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