DG-Mix: Domain Generalization for Anomalous Sound Detection Based on Self-Supervised Learning

Detecting anomalies in sound data has recently received significant attention due to the increasing number of implementations of sound condition monitoring solutions for critical assets. In this context, changing operating conditions impose significant domain shifts resulting in performance drops if a model trained on a set of operating conditions is applied to a new operating condition. An essential challenge is distinguishing between anomalies due to faults and new operating conditions. Therefore, the high variability of operating conditions or even the emergence of new operating conditions requires algorithms that can be applied under all conditions. Therefore, domain generalization approaches need to be developed to tackle this challenge. In this paper, we propose a novel framework that leads to a representation that separates the health state from changes in operating conditions in the latent space. This research introduces DG-Mix (Domain Generalization Mixup), an algorithm inspired by the recent Variance-Invariance-Covariance Regularization (VICReg) framework. Extending the original VI-CReg algorithm, we propose to use Mixup between two samples of the same machine type as a transformation and apply a geometric constraint instead of an invariance loss. This approach allows us to learn a representation that distinguishes between the operating conditions in an unsupervised way. The proposed DG-Mix enables the generalization between different machine types and diverse operating conditions without an additional adaptation of the hyperparameters or an ensemble method. DG-Mix provides superior performance and outperforms the baselines on the development dataset of DCASE 2022 challenge task 2. We also demonstrate that training using DG-Mix and then fine-tuning the model to a specific task significantly improves the model’s performance.