Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection

@article{akir2017ConvolutionalRN,
  title={Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection},
  author={Emre Çakir and Giambattista Parascandolo and Toni Heittola and H. Huttunen and Tuomas Virtanen},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  year={2017},
  volume={25},
  pages={1291-1303}
}
Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks (CNNs) are able to extract higher level features that are invariant to local spectral and temporal variations. Recurrent neural networks (RNNs) are powerful in learning the longer term temporal context in the audio signals. CNNs and RNNs as classifiers have recently shown improved performances over established methods in… Expand
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