通过单数值分解和最佳收缩,对同步自发的音声发射进行了可靠的估计
1Department of Electrical Engineering, National Tsing Hua University, Hsinchu City, 300044, Taiwanjeremy.0515@gapp.nthu.edu.tw, ywliu@ee.nthu.edu.tw.
JASA express letters
|November 8, 2023
概括
矩阵信号处理增强了对噪声的耳声波排放 (OAE) 估计. 与时间域方法相比,汉克尔矩阵方法在同步自发OAE方面表现优越.
科学领域:
- 生物医学工程 生物医学工程
- 声学 声学 声学 声学
- 信号处理 信号处理
背景情况:
- 耳声排放 (OAEs) 对于听力诊断至关重要.
- 在杂环境中估计OAEs存在重大挑战.
- 基于矩阵的信号处理为改进OAE检测提供了潜力.
研究的目的:
- 评估矩阵信号处理技术,以估计在噪音条件下产生的音响排放 (OAEs).
- 为了比较使用最佳收缩的时间域和频域 (汉克尔矩阵) 方法的性能.
- 分析这些方法对同步自发性OAE与短暂唤起的OAE的有效性.
主要方法:
- 在数据矩阵中存储重复点击响应.
- 在时间或频率领域应用单数值分解 (SVD).
- 在每个频率上构建汉克尔矩阵,用于频域分析.
- 在单数值上利用最佳收缩 (OS) 来最大限度地提高信号噪声比 (SNR).
主要成果:
- 与时间域OS方法相比,汉克尔矩阵方法在估计同步自发的耳声排放 (OAE) 中表现优越.
- 暂时唤起的耳声排放估计方法之间没有观察到显著的性能差异.
- 对人类OAE数据的分析验证了针对特定OAE类型的汉克尔矩阵方法的优越性.
结论:
- 矩阵信号处理,特别是汉克尔矩阵方法,在噪音环境中有效提高同步自发OAE的估计.
- 方法之间的性能差异表明频域分析对于某些OAE特征更有利.
- 需要进一步调查,以了解性能差异背后的原因,并优化OAE估计技术.
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