论文标题
分析异常声音检测的特征表示
Analysis of Feature Representations for Anomalous Sound Detection
论文作者
论文摘要
在这项工作中，我们彻底评估了预审预测的神经网络作为异常声音检测的特征提取器的功效。为此，我们利用这些神经网络中包含的知识来提取语义上丰富的特征（表示），这些特征是对高斯混合模型的输入，该模型被用作密度估计器来建模正态性。我们比较了对来自各个域的数据培训的功能提取器，即图像，环境声音和音乐。我们的方法对来自阀门，泵，滑块和风扇等工厂机械的录音进行了评估。所有评估的表示形式都优于自动编码器基线，而基于音乐的表示，在大多数情况下，表现出色。这些结果挑战了与特征提取器的域紧密匹配和下游任务的共同假设，从而可以提高下游任务性能。
In this work, we thoroughly evaluate the efficacy of pretrained neural networks as feature extractors for anomalous sound detection. In doing so, we leverage the knowledge that is contained in these neural networks to extract semantically rich features (representations) that serve as input to a Gaussian Mixture Model which is used as a density estimator to model normality. We compare feature extractors that were trained on data from various domains, namely: images, environmental sounds and music. Our approach is evaluated on recordings from factory machinery such as valves, pumps, sliders and fans. All of the evaluated representations outperform the autoencoder baseline with music based representations yielding the best performance in most cases. These results challenge the common assumption that closely matching the domain of the feature extractor and the downstream task results in better downstream task performance.
