Corpus ID: 449184

Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features

@article{Adavanne2017SoundED,
  title={Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features},
  author={Sharath Adavanne and Giambattista Parascandolo and Pasi Pertil{\"a} and Toni Heittola and Tuomas Virtanen},
  journal={ArXiv},
  year={2017},
  volume={abs/1706.02293}
}
In this paper, we propose the use of spatial and harmonic features in combination with long short term memory (LSTM) recurrent neural network (RNN) for automatic sound event detection (SED) task. Real life sound recordings typically have many overlapping sound events, making it hard to recognize with just mono channel audio. Human listeners have been successfully recognizing the mixture of overlapping sound events using pitch cues and exploiting the stereo (multichannel) audio signal available… Expand
SOUND EVENT DETECTION IN MULTICHANNEL AUDIO LSTM NETWORK
Sound event detection using spatial features and convolutional recurrent neural network
Multichannel Sound Event Detection Using 3D Convolutional Neural Networks for Learning Inter-channel Features
A report on sound event detection with different binaural features
Robust Polyphonic Sound Event Detection by Using Multi Frame Size Denoising Autoencoder
A neural network approach for sound event detection in real life audio
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 35 REFERENCES
Polyphonic sound event detection using multi label deep neural networks
Recurrent neural networks for polyphonic sound event detection in real life recordings
TUT database for acoustic scene classification and sound event detection
Environmental Sound Recognition With Time–Frequency Audio Features
Overlapping sound event recognition using local spectrogram features and the generalised hough transform
Metrics for Polyphonic Sound Event Detection
Acoustic event detection in real life recordings
Deep beamforming networks for multi-channel speech recognition
  • X. Xiao, Shinji Watanabe, +7 authors Dong Yu
  • Computer Science, Engineering
  • 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2016
NMF-based environmental sound source separation using time-variant gain features
Supervised model training for overlapping sound events based on unsupervised source separation
...
1
2
3
4
...