Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Recognition

@inproceedings{Takahashi2016DeepCN,
  title={Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Recognition},
  author={Naoya Takahashi and Michael Gygli and Beat Pfister and Luc Van Gool},
  booktitle={INTERSPEECH},
  year={2016}
}
We propose a novel method for Acoustic Event Recognition (AER). 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 AER, we introduce a convolutional neural network (CNN) with a large input field. In contrast to previous works, this enables to train audio event… CONTINUE READING
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