一个统一的束形和源分离模型,用于静态和动态的人机交互
Jorge Wuth1, Rodrigo Mahu1, Israel Cohen2
1Speech Processing and Transmission Laboratory, Department of Electrical Engineering, University of Chile, Av. Tupper 2007, Santiago, Chile.
JASA express letters
|March 5, 2024
概括
本研究介绍了一种统一的模型,用于结合光束成形和盲源分离 (BSS). 这种新的方法在具有挑战性的人机交互 (HRI) 场景中显著改善了信号噪声比 (SNR).
科学领域:
- 信号处理 信号处理
- 声学 声学 声学 声学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 将光束成形和盲源分离 (BSS) 结合起来,对于在杂环境中提高语音可理解性至关重要.
- 现有的方法经常在动态或复杂的声学环境中扎,例如人机交互 (HRI).
研究的目的:
- 提出一个整合beamforming和BSS的统一模型,以改善语音恢复.
- 在现实世界静态和动态HRI数据中评估模型的性能.
主要方法:
- 开发了一种统一的模型,将BSS与最小偏差无扭曲响应 (MVDR) 束造型器相结合.
- 使用Oracle信息验证的模型假设用于准确的目标语音恢复.
- 在真实静态HRI数据上测试系统,并在动态HRI环境中分析性能.
主要成果:
- 统一模型在静态HRI数据上实现了较高的信号噪声比率 (SNR),相比之前的平行和级联系统.
- 在动态HRI环境中,拟议系统的SNR增益比级联系统大2.8dB.
- 在动态HRI环境中,并行组合被发现是不可行的.
结论:
- 统一模型为结合BSS和梁成型提供了一种优越的方法.
- 该方法在具有挑战性的HRI场景中有效地提高了语音信号质量.
- 这种集成对动态和难以建模的声学环境特别有益.
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