  Automated audio captioning (AAC) is the task of automatically creating
textual descriptions (i.e. captions) for the contents of a general audio
signal. Most AAC methods are using existing datasets to optimize and/or
evaluate upon. Given the limited information held by the AAC datasets, it is
very likely that AAC methods learn only the information contained in the
utilized datasets. In this paper we present a first approach for continuously
adapting an AAC method to new information, using a continual learning method.
In our scenario, a pre-optimized AAC method is used for some unseen general
audio signals and can update its parameters in order to adapt to the new
information, given a new reference caption. We evaluate our method using a
freely available, pre-optimized AAC method and two freely available AAC
datasets. We compare our proposed method with three scenarios, two of training
on one of the datasets and evaluating on the other and a third of training on
one dataset and fine-tuning on the other. Obtained results show that our method
achieves a good balance between distilling new knowledge and not forgetting the
previous one.
