  Audio analysis is useful in many application scenarios. The state-of-the-art
audio analysis approaches assume the data distribution at training and
deployment time will be the same. However, due to various real-life challenges,
the data may encounter drift in its distribution or can encounter new classes
in the late future. Thus, a one-time trained model might not perform
adequately. Continual learning (CL) approaches are devised to handle such
changes in data distribution. There have been a few attempts to use CL
approaches for audio analysis. Yet, there is a lack of a systematic evaluation
framework. In this paper, we create a comprehensive CL dataset and characterize
CL approaches for audio-based monitoring tasks. We have investigated the
following CL and non-CL approaches: EWC, LwF, SI, GEM, A-GEM, GDumb, Replay,
Naive, Cumulative, and Joint training. The study is very beneficial for
researchers and practitioners working in the area of audio analysis for
developing adaptive models. We observed that Replay achieved better results
than other methods in the DCASE challenge data. It achieved an accuracy of
70.12% for the domain incremental scenario and an accuracy of 96.98% for the
class incremental scenario.
