Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning

Robert Müller, Fabian Ritz, Steffen Illium, Claudia Linnhoff-Popien

Abstract

In industrial applications, the early detection of malfunctioning factory machinery is crucial. In this paper, we consider acoustic malfunction detection via transfer learning. Contrary to the majority of current approaches which are based on deep autoencoders, we propose to extract features using neural networks that were pre-trained on the task of image classification. We then use these features to train a variety of anomaly detection models and show that this improves results compared to convolutional autoencoders in recordings of four different factory machines in noisy environments. Moreover, we find that features extracted from ResNet based networks yield better results than those from AlexNet and Squeezenet. In our setting, Gaussian Mixture Models and One-Class Support Vector Machines achieve the best anomaly detection performance.

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Paper Citation


in Harvard Style

Müller R., Ritz F., Illium S. and Linnhoff-Popien C. (2021). Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 49-56. DOI: 10.5220/0010185800490056


in Bibtex Style

@conference{icaart21,
author={Robert Müller and Fabian Ritz and Steffen Illium and Claudia Linnhoff-Popien},
title={Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={49-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010185800490056},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning
SN - 978-989-758-484-8
AU - Müller R.
AU - Ritz F.
AU - Illium S.
AU - Linnhoff-Popien C.
PY - 2021
SP - 49
EP - 56
DO - 10.5220/0010185800490056