性能加权的混合光谱图
Jeff Tucker1, Kathleen E Wage1, John R Buck2
1Electrical and Computer Engineering Department, George Mason University, Fairfax, Virginia 22030, USA.
The Journal of the Acoustical Society of America
|March 26, 2025
概括
本研究介绍了性能加权混合 (PWB) 谱图,这是用于时间频率分析的自适应算法. PWB光谱图有效地分离信号并识别静音源,即使计算资源有限.
科学领域:
- 信号处理 信号处理
- 时间频率分析
- 机器学习 机器学习
背景情况:
- 传统的光谱图基于渐变的短时间里叶变换,涉及分辨率和干扰抑制之间的权衡.
- 适应光谱算法通过调整波器库来调整输入数据来提高性能,但往往需要大量的计算资源.
- 现有的自适应方法对于具有有限计算能力的应用,如自主系统,具有挑战.
研究的目的:
- 开发一种适应性光谱算法,适用于计算上受限制的应用.
- 为了增强近距离音调的分离和静音信号的检测.
- 为了降低与自适应时间频率分析相关的计算成本.
主要方法:
- 开发了性能加权混合 (PWB) 谱图算法.
- PWB 结合了传统过器库的输出,具有不同的收缩度.
- 混合权重在每个时间频率点上都会动态调整.
主要成果:
- PWB光谱图有效地分离了响亮的,距离很近的音调.
- 静音信号被成功识别,即使存在噪音.
- 与其他自适应方法相比,该算法显示了显著较低的计算要求.
- 对水下滑翔机数据的分析证实了PWB谱图能够在车辆噪音中检测出安静的声信号.
结论:
- 在资源有限的环境中,PWB光谱为时间频率分析提供了高效的适应性解决方案.
- 这种算法提供了一种实际的方法来改善信号检测和分离,而无需过度的计算开销.
- PWB谱图非常适合用于自动驾驶汽车和水下声学等应用.
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