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DSENet:用于边缘设备上的听力改善的定向信号提取网络
Anton Kovalyov1, Kashyap Patel1, Issa Panahi1
1Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA.
概括
我们开发了一个定向信号提取网络 (DSENet),用于实时的音频处理. 这种低延迟网络有效地从杂的环境中提取所需的声音,改善边缘设备的听力.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 声学 声学 在声学上
背景情况:
- 麦克风阵列捕捉复杂的声音景观与反响和多个来源.
- 传统的光束成形方法经常受到交叉声和延迟问题的困扰.
- 现有的局限空间的方法可能不适合实时应用.
研究的目的:
- 为实时音频处理提出一个新的定向信号提取网络 (DSENet).
- 解决现有方法在处理反响和多源环境中的局限性.
- 在边缘设备上实现实际的听力改善解决方案.
主要方法:
- DSENet在时间域中使用了计算效率高,低扭曲的线性模型.
- 该网络的设计是为了实现低延迟的实时性能.
- 它从特定的感兴趣方向区域提取信号,同时处理多个源.
主要成果:
- DSENet的性能优于预言光束构造器和最先进的低延迟因果语音分离方法.
- 该系统实现了非常低的延迟时间,仅为4毫秒.
- 在智能手机上成功实时部署证明了实际可行性.
结论:
- DSENet提供了一种实用且有效的解决方案,用于在反响条件下提取定向信号.
- 它的低延迟和计算效率使它成为边缘设备上的助听器的理想选择.
- 拟议的方法绕过了传统光束成型中固有的交叉通道问题.
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