Filterbank learning for deep neural network based polyphonic sound event detection

@article{akir2016FilterbankLF,
  title={Filterbank learning for deep neural network based polyphonic sound event detection},
  author={Emre Çakir and Ezgi C. Ozan and Tuomas Virtanen},
  journal={2016 International Joint Conference on Neural Networks (IJCNN)},
  year={2016},
  pages={3399-3406}
}
Deep learning techniques such as deep feedforward neural networks and deep convolutional neural networks have recently been shown to improve the performance in sound event detection compared to traditional methods such as Gaussian mixture models. One of the key factors of this improvement is the capability of deep architectures to automatically learn higher levels of acoustic features in each layer. In this work, we aim to combine the feature learning capabilities of deep architectures with the… Expand
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