实时信号处理用于分布式声学传感和声学传感系统在非静止噪声下
Samuel Yaw Mensah1, Tao Zhang2, Xin Zhao2
1School of Information Engineering, Tianjin University, 92 Weijin Road, Nankai District, Tianjin 300072, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
这项研究引入了一种统一的贝叶斯-卡尔曼估计器 (UBKE),用于在挑战非静止噪声时实时声学增强. 该UBKE自适应地融合了光谱和时间信息,显著提高了低延迟的信号质量.
科学领域:
- 信号处理 信号处理
- 声学 声学 在声学方面
- 机器学习 机器学习
背景情况:
- 在非静止噪声中实时声学增强对于因果,低延迟系统来说是困难的.
- 现有的方法很难有效地平衡光谱和时间信息.
研究的目的:
- 提出一个统一的贝叶斯-卡尔曼估计器 (UBKE) 用于因果,低延迟的声信号增强.
- 分析融合光谱和时间估计技术,以适应降噪.
主要方法:
- 通过将贝叶斯最小平均平方误差 (MMSE) 估计器与卡尔曼状态空间跟踪器集成,开发了一个封闭形式的UBKE.
- 使用差异最佳的聚变重量 (α(k)) 来适应平衡光谱和时间信息.
- 分析了理论属性,包括偏差差异,稳定性和性能指标 (SNR,日志光谱扭曲).
主要成果:
- UBKE以16毫秒的延迟和实时系数低于0.5.5的因果关系运行.
- 在非静止噪声中实现了高达+9.8dB的SNR改进和~17%的PESQ比基线MMSE提高.
- 演示了分析预测和语音体的经验结果之间的密切一致.
结论:
- UBKE提供了一个可解释的,低延迟的框架,用于实时声学传感和语音增强.
- 该方法以适应性平衡光谱和时间信息,优于传统的MMSE估计器.
- 作为未来混合模型驱动和学习增强声学系统的基础.
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