Improving Continual Learning of Acoustic Scene Classification via Mutual Information Optimization

Continual learning, which aims to incrementally accumulate knowledge, has been an increasingly significant but challenging research topic for deep models that are prone to catastrophic forgetting. In this paper, we propose a novel replay-based continual learning approach in the context of class-incremental learning in acoustic scene classification, to classify audio recordings into an expanding set of classes that characterize the acoustic scenes. Our approach is improving both the modeling and memory selection mechanism via mutual information optimization in continual learning. By regarding incremental classes of acoustic scenes as different tasks, our model is expected to learn both task-agnostic and task-specific knowledge by replaying representative and informative samples. This optimization also enables the model to utilize past knowledge effectively and learn from new information during continual learning. We demonstrate that our approach has a superior performance compared to existing methods on multiple datasets and continual learning evaluation metrics.