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

Abstract

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.