# Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning

Source: [Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning](<https://www.sota2.com/research/paper/68fa694b1e209131cea21494>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:57:51.173Z

Authors: Robert M\\"uller, Fabian Ritz, Steffen Illium, Claudia Linnhoff-Popien

Published: 2020-06-05T13:29:12.000Z

## 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 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2006.03429>)

[PDF](<https://arxiv.org/pdf/2006.03429.pdf>)