在线持续学习在声学场景分类:一个实证研究
Donghee Ha1,2, Mooseop Kim1,2, Chi Yoon Jeong1,2
1Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Daejeon 34129, Republic of Korea.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究评估了十种持续学习 (CL) 方法用于声场景分类 (ASC). 在不同的场景中,SCR和GDumb表现最好,为未来的CL研究提供了声音分析的指导.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 深度学习提升了声场景分类 (ASC) 对声音事件的准确性.
- 持续学习 (CL) 方法用于在ASC中调整模型以适应不断变化的任务尚未得到充分探索.
- 需要对现有的CL方法进行系统的性能分析,以指导未来的研究.
研究的目的:
- 系统地分析和比较10个最近的ASC的CL方法的性能.
- 根据不同的学习场景和内存缓冲区大小,为选择合适的CL方法提供准则.
- 在现实的在线类增量 (OCI) 和在线域增量 (ODI) 设置中评估CL方法.
主要方法:
- 评估了十种CL方法:两种基于规范化和八种基于重复.
- 在三个公共声音数据集中定义并使用了OCI和ODI场景.
- 根据平均准确度,平均遗忘和训练时间来评估绩效.
主要成果:
- 在OCI场景中,iCaRL和SCR在小缓冲区中表现出色;GDumb在大缓冲区中表现最好.
- 在ODI场景中,有监督对比学习的SCR始终优于其他类型的SCR,无论缓冲区的规模如何.
- 基于重复的方法显示了一致的训练时间和性能增长,具有更大的内存缓冲区.
结论:
- 建议GDumb和SCR作为ASC持续学习的主要考虑因素.
- 方法选择应取决于特定的OCI或ODI场景和可用的内存缓冲区大小.
- 进一步的研究可以在这些发现的基础上建立更强大,更适应的ASC系统.
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