论文标题
深度自动编码基于GMM的声音信号中的无监督异常检测及其超参数优化
Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization
论文作者
论文摘要
对于公司而言，出厂机械的故障或故障可能会昂贵，因此对自动机器检查的需求增加。现有的基于声学信号的无监督异常检测的方法，例如使用深度自动编码器（DA）或高斯混合模型（GMM）的方法，具有较差的异常检测性能。在这项工作中，我们提出了一种基于具有高参数优化（DAGMM-HO）的深度自动编码高斯混合模型的新方法。在我们的方法中，DAGMM-HO首次将常规DAGMM应用于音频域，并认为其对降低尺寸和统计建模的总优化将改善异常检测性能。此外，DAGMM-HO通过基于GAP统计量和累积特征值进行超参数优化，解决了常规DAGMM的高参数灵敏度问题。我们对工业风扇的实验数据对拟议方法的评估表明，它的表现明显优于先前的方法，并根据标准AUC得分实现了20％的提高。
Failures or breakdowns in factory machinery can be costly to companies, so there is an increasing demand for automatic machine inspection. Existing approaches to acoustic signal-based unsupervised anomaly detection, such as those using a deep autoencoder (DA) or Gaussian mixture model (GMM), have poor anomaly-detection performance. In this work, we propose a new method based on a deep autoencoding Gaussian mixture model with hyper-parameter optimization (DAGMM-HO). In our method, the DAGMM-HO applies the conventional DAGMM to the audio domain for the first time, with the idea that its total optimization on reduction of dimensions and statistical modelling will improve the anomaly-detection performance. In addition, the DAGMM-HO solves the hyper-parameter sensitivity problem of the conventional DAGMM by performing hyper-parameter optimization based on the gap statistic and the cumulative eigenvalues. Our evaluation of the proposed method with experimental data of the industrial fans showed that it significantly outperforms previous approaches and achieves up to a 20% improvement based on the standard AUC score.
