Audio analysis is useful in many application scenarios. The state-of-the-art audio analysis approaches assume that the data distribution at training and deployment time will be the same. However, due to various real-life environmental factors, 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. In this paper, we characterize continual learning (CL) approaches in audio analysis. In this paper, we characterize continual learning (CL) approaches, intended to tackle catastrophic forgetting arising due to drifts. As there is no CL dataset for audio analysis, we use DCASE 2020 to 2023 datasets to create various CL scenarios 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.

音频分析中的连续学习方法用于解决因分布漂移导致的灾难性遗忘问题，通过使用DCASE数据集探索了多个连续学习方法，在领域逐步增加和类别逐步增加的场景下，Replay方法取得了最佳结果，分别达到了70.12%和96.98%的准确率。

关于音频分析的持续学习情境和策略的特征化