Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks

• URL: http://arxiv.org/abs/2002.01107v2
• Date: Wed, 5 Feb 2020 02:27:12 GMT
• Title: Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks
• Authors: Chengwei Chen and Pan Chen and Lingyu Yang and Jinyuan Mo and Haichuan Song and Yuan Xie and Lizhuang Ma
• Abstract summary: It suffers from the class imbalance issue and the lacking in the abnormal instances. In this paper, a novel Gaussian Mixture Generative Adrial Network (GMGAN) is proposed under semi-supervised learning framework. Experiments show that our model has clear superiority over previous methods, and achieves the state-of-the-art results on DCASE dataset.
• Score: 30.970377781506258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Acoustic anomaly detection aims at distinguishing abnormal acoustic signals from the normal ones. It suffers from the class imbalance issue and the lacking in the abnormal instances. In addition, collecting all kinds of abnormal or unknown samples for training purpose is impractical and timeconsuming. In this paper, a novel Gaussian Mixture Generative Adversarial Network (GMGAN) is proposed under semi-supervised learning framework, in which the underlying structure of training data is not only captured in spectrogram reconstruction space, but also can be further restricted in the space of latent representation in a discriminant manner. Experiments show that our model has clear superiority over previous methods, and achieves the state-of-the-art results on DCASE dataset.

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