Sketching-Based Acoustic Scene Change Detection in Low-Power Embedded Devices

Acoustic scene classification (ASC) in embedded devices (EDs) is a challenging task due to a large variety of acoustic environments occurring in daily life. In particular, ASC is error-prone in scenes which are not well represented in its training data. In ultra low-power EDs such as hearing aids, the performance of an embedded classifier is additionally constrained by limited computational resources. This calls for the development of classifiers that are tailored to the specific environment of individual users. Therefore, we propose an online scene change detection (SCD) framework that aggregates statistical information of extracted low-dimensional audio features in the ED and detects change-points such that data of previously unseen scenes can be aggregated in the ED and further be evaluated and used for re-training in a cloud-based system. We compare an approach based on second-order statistics with a method based on sketching and show that both achieve significantly better overall SCD performance with respect to a low-complexity baseline. Furthermore, the sketching-based approach provides a better detection-error trade-off than the method relying on second-order statistics only.