Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning

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 n...

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Bibliographic Details
Published in:arXiv.org 2020-12
Main Authors: Müller, Robert, Ritz, Fabian, Illium, Steffen, Linnhoff-Popien, Claudia
Format: Article
Language:English
Subjects:
Acoustic noise
Acoustics
Anomalies
Feature extraction
Image classification
Image detection
Industrial applications
Learning
Neural networks
Probabilistic models
Support vector machines
Online Access:Get full text
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Summary: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 pretrained 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.
ISSN:2331-8422
DOI:10.48550/arxiv.2006.03429