Anomaly Detection Based on an Ensemble of Dereverberation and Anomalous Sound Extraction

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 p...

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Bibliographic Details
Published inICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 865 - 869
Main Authors Kawaguchi, Yohei, Tanabe, Ryo, Endo, Takashi, Ichige, Kenji, Hamada, Koichi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2019
Subjects
anomalous sound extraction
anomaly detection
dereverberation
ensemble
machine health monitoring
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8683702