Noise Robust Acoustic Anomaly Detection System with Nonnegative Matrix Factorization Based on Generalized Gaussian Distribution

Akihito AIBA  Minoru YOSHIDA  Daichi KITAMURA  Shinnosuke TAKAMICHI  Hiroshi SARUWATARI  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E104-D    No.3    pp.441-449
Publication Date: 2021/03/01
Publicized: 2020/12/18
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020EDK0002
Type of Manuscript: PAPER
Category: Speech and Hearing
Keyword: 
nonnegative matrix factorization,  generalized Gaussian distribution,  anomaly detection,  outlier detection,  

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Summary: 
We studied an acoustic anomaly detection system for equipments, where the outlier detection method based on recorded sounds is used. In a real environment, the SNR of the target sound against background noise is low, and there is the problem that it is necessary to catch slight changes in sound buried in noise. In this paper, we propose a system in which a sound source extraction process is provided at the preliminary stage of the outlier detection process. In the proposed system, nonnegative matrix factorization based on generalized Gaussian distribution (GGD-NMF) is used as a sound source extraction process. We evaluated the improvement of the anomaly detection performance in a low-SNR environment. In this experiment, SNR capable of detecting an anomaly was greatly improved by providing GGD-NMF for preprocessing.