Abstract:

We introduce a framework to perform unsupervised anomalous sound detection by leveraging embeddings learned in hyperbolic space. Previously, hyperbolic spaces have demonstrated the abil- ity to encode hierarchical relationships much more effectively than Euclidean space when using those embeddings for classification. A corollary of that property is that the distance of a given embedding from the hyperbolic space origin encodes a notion of classification certainty, naturally mapping inlier class samples to the space edges and outliers near the origin. As such, we expect the hyperbolic em- beddings generated by a deep neural network pre-trained to classify short-time Fourier transform frames of normal machine sounds to be more distinctive than Euclidean embeddings when attempting to identify unseen anomalous data. In particular, we show here how to perform unsupervised anomaly detection using embeddings from a trained modified MobileFaceNet architecture with a hyperbolic em- bedding layer, using the embeddings generated from a test sample to generate an anomaly score. Our results show that the proposed approach outperforms similar methods in Euclidean space on the DCASE 2022 Unsupervised Anomalous Sound Detection dataset.