• DOI:10.1109/ICASSP.2015.7178320
Corpus ID: 206741918

A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks

@article{Marchi2015ANA,
  title={A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks},
  author={E. Marchi and Fabio Vesperini and F. Eyben and S. Squartini and B. Schuller},
  journal={2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2015},
  pages={1996-2000}
}

E. Marchi, Fabio Vesperini, +2 authors B. Schuller

Published 2015

Computer Science

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Acoustic novelty detection aims at identifying abnormal/novel acoustic signals which differ from the reference/normal data that the system was trained with. In this paper we present a novel unsupervised approach based on a denoising autoencoder. In our approach auditory spectral features are processed by a denoising autoencoder with bidirectional Long Short-Term Memory recurrent neural networks. We use the reconstruction error between the input and the output of the autoencoder as activation… CONTINUE READING

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