Unsupervised acoustic condition monitoring with Riemannian geometry

In this paper, we present an unsupervised method for acoustic condition monitoring. Our method relies on the Riemannian geometry of symmetric and positive-definite (SPD) matrices. Specifically, SPD matrices enable us to build features for multi-channel data, which naturally encode the mutual relationships between the channels. By exploiting the Riemannian geometry of SPD matrices, we show that these features encompass informative comparisons. The proposed anomaly score is then based on a one-class SVM applied to the proposed features and their induced Riemannian distance. We test the proposed method on two benchmarks and show that it achieves state-of-the-art results. In addition, we demonstrate the robustness of the proposed method to noise and to low sampling rates.