GFANC-RL:基于强化学习的生成式固定过器主动噪声控制
Zhengding Luo1, Haozhe Ma2, Dongyuan Shi1
1Digital Signal Processing Lab, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
概括
本研究引入了强化学习 (GFANC-RL) 的生成式固定过器主动噪声控制,消除了手动数据标签,以改善降噪. 这种新的方法提高了卷积神经网络 (CNN) 的准确性和系统性能.
科学领域:
- 声学 声学 在声学上
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 生成式固定过器主动噪声控制 (GFANC) 提供了降噪与稳定之间的平衡.
- 训练GFANC的卷积神经网络 (CNN) 需要广泛的,准确标记的噪音数据,这是资源密集的,容易出现错误.
- 标签不准确可能会严重损害GFANC系统中CNN的过器生成精度.
研究的目的:
- 提出一种新的基于强化学习的GFANC (GFANC-RL) 方法.
- 消除在GFANC系统中需要手动标记噪声数据的需要.
- 通过自动学习来提高GFANC方法的准确性和效率.
主要方法:
- 开发了一个GFANC-RL框架,利用强化学习 (RL) 来自动化CNN参数更新.
- 采用RL的探索能力来绕过数据标签过程.
- 使用RL算法解决了与GFANC中的二进制组合权重相关的非可区分性挑战.
主要成果:
- 模拟结果证实了GFANC-RL方法的有效性.
- 证明了GFANC-RL在处理现实世界记录的噪音中的可转移性.
- 在多种不同的声学路径中验证了性能,展示了强度.
结论:
- 在GFANC培训中,GFANC-RL成功地消除了对标记数据的依赖.
- 拟议的方法实现了精确的降噪,并保持了系统的稳定性.
- 对于主动噪声控制应用,GFANC-RL提供了一个更高效,更强大的解决方案.
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