The search functionality is under construction.
The search functionality is under construction.

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

  • Full Text Views

    0

  • Cite this

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.

Publication
IEICE TRANSACTIONS on Information 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

Authors

Akihito AIBA
  Ricoh Company Ltd.
Minoru YOSHIDA
  Ricoh Company Ltd.
Daichi KITAMURA
  Kagawa College,University of Tokyo
Shinnosuke TAKAMICHI
  University of Tokyo
Hiroshi SARUWATARI
  University of Tokyo

Keyword