IEICE TRANSACTIONS on Information
Noise Robust Acoustic Anomaly Detection System with Nonnegative Matrix Factorization Based on Generalized Gaussian Distribution
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
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Akihito AIBA, Minoru YOSHIDA, Daichi KITAMURA, Shinnosuke TAKAMICHI, Hiroshi SARUWATARI, "Noise Robust Acoustic Anomaly Detection System with Nonnegative Matrix Factorization Based on Generalized Gaussian Distribution" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 3, pp. 441-449, March 2021, doi: 10.1587/transinf.2020EDK0002.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDK0002/_p
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@ARTICLE{e104-d_3_441,
author={Akihito AIBA, Minoru YOSHIDA, Daichi KITAMURA, Shinnosuke TAKAMICHI, Hiroshi SARUWATARI, },
journal={IEICE TRANSACTIONS on Information},
title={Noise Robust Acoustic Anomaly Detection System with Nonnegative Matrix Factorization Based on Generalized Gaussian Distribution},
year={2021},
volume={E104-D},
number={3},
pages={441-449},
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.},
keywords={},
doi={10.1587/transinf.2020EDK0002},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Noise Robust Acoustic Anomaly Detection System with Nonnegative Matrix Factorization Based on Generalized Gaussian Distribution
T2 - IEICE TRANSACTIONS on Information
SP - 441
EP - 449
AU - Akihito AIBA
AU - Minoru YOSHIDA
AU - Daichi KITAMURA
AU - Shinnosuke TAKAMICHI
AU - Hiroshi SARUWATARI
PY - 2021
DO - 10.1587/transinf.2020EDK0002
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2021
AB - 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.
ER -
English
