End-to-End Polyphonic Sound Event Detection Using Convolutional Recurrent Neural Networks with Learned Time-Frequency Representation Input

@article{Cakir2018EndtoEndPS,
  title={End-to-End Polyphonic Sound Event Detection Using Convolutional Recurrent Neural Networks with Learned Time-Frequency Representation Input},
  author={Emre Cakir and Tuomas Virtanen},
  journal={2018 International Joint Conference on Neural Networks (IJCNN)},
  year={2018},
  pages={1-7}
}
  • Emre Cakir, Tuomas Virtanen
  • Published 2018
  • Computer Science, Engineering, Mathematics
  • 2018 International Joint Conference on Neural Networks (IJCNN)
  • Sound event detection systems typically consist of two stages: extracting hand-crafted features from the raw audio waveform, and learning a mapping between these features and the target sound events using a classifier. [...] Key Method The feature extraction over the raw waveform is conducted by a feedforward layer block, whose parameters are initialized to extract the time-frequency representations.Expand Abstract
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