  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.
