Anomaly Detection Based on an Ensemble of Dereverberation and Anomalous Sound Extraction
Yohei Kawaguchi, Ryo Tanabe, Takashi Endo, Kenji Ichige, Koichi Hamada
Abstract
To develop a sound-monitoring system for checking machine health, a method for detecting anomalous sounds is proposed. In real environments such as factories, reverberation and background noise are mixed in an observed signal, so detection performance is degraded. It can be expected that detection performance will be improved by using a front-end algorithm for acoustic signal processing such as dereverberation and denoising. However, any algorithm has pros and cons, so it is not possible to choose the best front-end algorithm only. To solve this problem, the proposed method is based on a front-end ensemble consisting of a blind-dereverberation algorithm and multiple anomalous-sound-extraction algorithms. Experimental results indicate that the proposed method improves detection performance significantly.
BibTeX
@inproceedings{icassp2019_anomalydetection,
title = {Anomaly Detection Based on an Ensemble of Dereverberation and Anomalous Sound Extraction},
author = {Yohei Kawaguchi and Ryo Tanabe and Takashi Endo and Kenji Ichige and Koichi Hamada},
booktitle = {ICASSP 2019},
year = {2019}
}