使用主要组件分析对非静止的低马赫数空气声学源进行时间域表征
Mitchell J Swann1, Zachary W Yoas1, Adam S Nickels1
1The Pennsylvania State University, State College, Pennsylvania 16804, USA.
The Journal of the Acoustical Society of America
|September 3, 2024
概括
主要组件分析 (PCA) 提供了一种数据驱动的方法,用于消除来自冲动性空声源的麦克风阵列信号. 这种方法优于传统方法,特别是在信号噪声比较低的条件下.
科学领域:
- 声学 声学 在声学方面
- 信号处理 信号处理
- 流体动力学 流体动力学
背景情况:
- 传统的空声源表征方法通常需要假设或用户干预,特别是在低信号噪声比 (SNR) 的环境中.
- 冲动和非静止的空声源,如环/边缘相互作用,需要时间域信号处理技术.
研究的目的:
- 介绍和验证主要组件分析 (PCA) 对于时间域麦克风阵列消音的应用.
- 用数据驱动的,无动于衷的信号处理方法来表征冲动性空声源.
- 为了比较PCA性能与传统集体平均方法.
主要方法:
- 利用矩阵分解技术的主要组件分析 (PCA) 来消除时间域麦克风阵列信号的噪声.
- 专注于信号的时间对齐,以促进PCA应用.
- 研究的PCA用于描述来自环/边缘相互作用的空气声学排放.
主要成果:
- 在PCA的研究中,PCA证明了对冲动性空声源的有效描述和表征.
- 结果与理论预测和先前验证的研究有很好的一致性.
- PCA的性能超过了整体平均值,特别是在没有用户干预的情况下,直到一定SNR极限.
结论:
- PCA为时间域空声源表征提供了一个强大的,数据驱动的替代方案,减少了对用户干预的需求.
- 一个已识别的SNR极限表明PCA的有效性可能在特定问题上下降的条件.
- 这些发现有助于设计涉及冲动性空声源和麦克风阵列的未来实验.
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