Recurrent neural networks for polyphonic sound event detection in real life recordings

@article{Parascandolo2016RecurrentNN,
  title={Recurrent neural networks for polyphonic sound event detection in real life recordings},
  author={Giambattista Parascandolo and H. Huttunen and Tuomas Virtanen},
  journal={2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2016},
  pages={6440-6444}
}
In this paper we present an approach to polyphonic sound event detection in real life recordings based on bi-directional long short term memory (BLSTM) recurrent neural networks (RNNs). A single multilabel BLSTM RNN is trained to map acoustic features of a mixture signal consisting of sounds from multiple classes, to binary activity indicators of each event class. Our method is tested on a large database of real-life recordings, with 61 classes (e.g. music, car, speech) from 10 different… Expand
SOUND EVENT DETECTION IN REAL LIFE AUDIO USING MULTI-MODEL SYSTEM
Duration-Controlled LSTM for Polyphonic Sound Event Detection
Using Sequential Information in Polyphonic Sound Event Detection
BLSTM-HMM hybrid system combined with sound activity detection network for polyphonic Sound Event Detection
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
A Deep Neural Network-Driven Feature Learning Method for Polyphonic Acoustic Event Detection from Real-Life Recordings
Multi-Scale Recurrent Neural Network for Sound Event Detection
  • Rui Lu, Zhiyao Duan, C. Zhang
  • Computer Science
  • 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2018
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