基于数据驱动的源定位的视角依赖的语音动态
Weidi Wang1, Amir Ashkan Mokhtari2, Ankit Srivastava2
1Department of Mechanical Engineering, University of Massachusetts, Lowell, Lowell, Massachusetts 01854, USA.
The Journal of the Acoustical Society of America
|November 8, 2023
概括
这项研究使用语音阵列和特殊点来准确定位声音源. 人工神经网络准确地识别事件角度,优于传统方法.
科学领域:
- 声学 声学 在声学方面
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 在各种传感应用中,源角度定位问题至关重要.
- 音声晶体中的特殊点提供了独特的模态行为,以提高灵敏度.
- 现有的本地化方法可以通过先进的信号处理技术来改进.
研究的目的:
- 调查使用声波阵列和异常点进行二维弹性波散射分析.
- 为准确的源角度定位开发数据驱动的方法.
- 为了提高现有的本地化技术的性能.
主要方法:
- 在音频阵列中分析等频带结构和角度依赖的模态行为.
- 利用特殊的点和关键角度来提高灵敏度.
- 用自身模式训练一个人工神经网络,用于深度学习散射动态.
主要成果:
- 训练有素的神经网络准确地分类散射信号的事件角度.
- 与标准的延迟和总和技术相比,拟议的方法表明了优越的本地化性能.
- 实现了最小的侧叶水平和抑制的主叶宽度.
结论:
- 声波阵列和特殊点为先进的传感提供了一个物理相关的平台.
- 使用人工神经网络的数据驱动方法显著改善了源角度定位.
- 这种方法为现有的本地化技术提供了强大的增强,利用带拓和自身模式.
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