Detailed info
Continual Learning for Automated Audio Captioning Using The Learning Without Forgetting Approach
| Authors | Jan Berg, Konstantinos Drossos |
| Title | Continual Learning for Automated Audio Captioning Using The Learning Without Forgetting Approach |
| Abstract | 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. |
| ISBN | TBD |
| Conference | Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021) |
| Date | 15/11/2021 |
| Location | Virtual |
| Year of Publication, Publisher | 2021 |
| Url | https://zenodo.org/record/5723053 |
| DOI | 10.5281/zenodo.5723053 |
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Funding
This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.
