  Continuously learning new classes without catastrophic forgetting is a
challenging problem for on-device environmental sound classification given the
restrictions on computation resources (e.g., model size, running memory). To
address this issue, we propose a simple and efficient continual learning
method. Our method selects the historical data for the training by measuring
the per-sample classification uncertainty. Specifically, we measure the
uncertainty by observing how the classification probability of data fluctuates
against the parallel perturbations added to the classifier embedding. In this
way, the computation cost can be significantly reduced compared with adding
perturbation to the raw data. Experimental results on the DCASE 2019 Task 1 and
ESC-50 dataset show that our proposed method outperforms baseline continual
learning methods on classification accuracy and computational efficiency,
indicating our method can efficiently and incrementally learn new classes
without the catastrophic forgetting problem for on-device environmental sound
classification.
