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一个新的混合适应式自负载过器和GRU-Net用于空调的直角曲管道中的主动噪声控制

Wenzhao Zhu^1,2, Zezheng Gu², Xiaoling Chen^1,2

¹Key Laboratory of Intelligent Control and Neural Information Processing, Education Ministry of China, Yanshan University, Qinhuangdao 066004, China.

Sensors (Basel, Switzerland)

|October 29, 2025

概括

一种新的混合动力主动噪声控制 (ANC) 方法结合了自适应自负载过-x最小平均平方 (ASL-FxLMS) 和卷积神经网络门反复单元 (CNN-GRU),以有效地减少康复室中的复杂波噪声.

科学领域:

声学和信号处理
生物医学工程生物医学工程
人工智能的人工智能

背景情况:

康复室的空调噪音会对患者的心理健康产生负面影响.
现有的单层主动噪声控制 (ANC) 过器与复杂的波噪声作斗争.
深度递归ANC方法面临实时性能限制.

研究的目的:

开发一个先进的ANC系统,以有效和实时减轻复杂的声噪声.
在康复环境中改善患者的舒适度和心理状态.
解决当前ANC技术在处理复杂噪声配置文件方面的局限性.

主要方法:

提出了一个新的混合网络,集成了一个改进的CNN-GRU,具有多头注意力和ASL-FxLMS算法.
优化了CNN-GRU网络,以减少计算负载和增强波噪声减弱.
改进的时间延迟估计器和自适应自负载FxLMS算法用于实时性能和处理噪声不确定性.
一个凸起的组合结构连接了ASL-FxLMS和CNN-GRU,以平衡衰减,强度和跟踪.

主要成果:

拟议的混合ASL-FxLMS和CNN-GRU网络在复杂的声噪声中显示出更好的衰减性能.
优化的CNN-GRU结构和改进的时间延迟估计器增强了实时ANC功能.
适应式自负载FxLMS算法有效地管理了不确定的噪声组件.
模拟和理论分析证实了该方法在平衡关键绩效指标方面的有效性.

结论:

混合ASL-FxLMS和CNN-GRU方法为敏感环境中复杂波噪声的主动噪声控制提供了卓越的解决方案.
这种方法增强了实时处理和噪声减弱,潜在地改善了患者康复环境.
该研究验证了将深度学习与适应性过集成为高级ANC应用程序的有效性.

关键词:

活动噪音控制活动噪音控制适应式自负载过器自负载过器混合型深度递归网络.语音和空调噪音的噪音.

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