In this paper, we propose a method for online domain-incremental learning of acoustic scene classification from a sequence of different locations. Simply training a deep learning model on a sequence of different locations leads to forgetting of previously learned knowledge. In this work, we only correct the statistics of the Batch Normalization layers of a model using a few samples to learn the acoustic scenes from a new location without any excessive training. Experiments are performed on acoustic scenes from 11 different locations, with an initial task containing acoustic scenes from 6 locations and the remaining 5 incremental tasks each representing the acoustic scenes from a different location. The proposed approach outperforms fine-tuning based methods and achieves an average accuracy of 48.8% after learning the last task in sequence without forgetting acoustic scenes from the previously learned locations.

翻译：本文提出了一种从不同位置序列中进行在线域增量学习的方法，用于声学场景分类。若直接在连续的不同位置序列上训练深度学习模型，会导致先前习得知识的遗忘。本工作中，我们仅利用少量样本修正模型批归一化层的统计量，即可学习新位置的声学场景而无需额外训练。实验在11个不同位置的声学场景上进行，初始任务包含6个位置的声学场景，其余5个增量任务各代表不同位置的声学场景。所提方法优于基于微调的方法，在顺序学习最终任务后取得了48.8%的平均准确率，且未遗忘先前学习位置的声学场景。