论文标题
基于图像传输学习的机器声音的声学异常检测
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning
论文作者
论文摘要
在工业应用中，早期发现工厂机械故障至关重要。在本文中，我们考虑通过转移学习来考虑声学故障检测。与基于深度自动编码器基于的大多数当前方法相反，我们建议使用根据图像分类任务鉴定的神经网络提取功能。然后，我们使用这些功能来训练各种异常检测模型，并表明与噪音环境中四台不同工厂机器的录音中的卷积自动编码器相比，这可以改善结果。此外，我们发现从基于RESNET的网络中提取的功能比Alexnet和Squeezenet产生的结果更好。在我们的环境中，高斯混合模型和一级支撑矢量计算机达到了最佳的异常检测性能。
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.
