  Numerous noise adaptation techniques have been proposed to fine-tune
deep-learning models in speech enhancement (SE) for mismatched noise
environments. Nevertheless, adaptation to a new environment may lead to
catastrophic forgetting of the previously learned environments. The
catastrophic forgetting issue degrades the performance of SE in real-world
embedded devices, which often revisit previous noise environments. The nature
of embedded devices does not allow solving the issue with additional storage of
all pre-trained models or earlier training data. In this paper, we propose a
regularization-based incremental learning SE (SERIL) strategy, complementing
existing noise adaptation strategies without using additional storage. With a
regularization constraint, the parameters are updated to the new noise
environment while retaining the knowledge of the previous noise environments.
The experimental results show that, when faced with a new noise domain, the
SERIL model outperforms the unadapted SE model. Meanwhile, compared with the
current adaptive technique based on fine-tuning, the SERIL model can reduce the
forgetting of previous noise environments by 52%. The results verify that the
SERIL model can effectively adjust itself to new noise environments while
overcoming the catastrophic forgetting issue. The results make SERIL a
favorable choice for real-world SE applications, where the noise environment
changes frequently.
