  In this paper, we work on a sound recognition system that continually
incorporates new sound classes. Our main goal is to develop a framework where
the model can be updated without relying on labeled data. For this purpose, we
propose adopting representation learning, where an encoder is trained using
unlabeled data. This learning framework enables the study and implementation of
a practically relevant use case where only a small amount of the labels is
available in a continual learning context. We also make the empirical
observation that a similarity-based representation learning method within this
framework is robust to forgetting even if no explicit mechanism against
forgetting is employed. We show that this approach obtains similar performance
compared to several distillation-based continual learning methods when employed
on self-supervised representation learning methods.
