通过自我监督的声学前置和关键值记忆来增强骨传导传感器信号
Changyan Zheng1,2, Hao He1,2, Xiaohu Fan2
1Defense Innovation Institute, Academy of Military Sciences, Beijing 100071, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
这项研究引入了一种新的自我监督学习 (SSL) 框架,以增强骨传导 (BC) 语音信号. 该方法有效地恢复丢失的高频信息,显著改善语音清晰度和可理解性.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 生物医学工程 生物医学工程
背景情况:
- 骨传导 (BC) 传感器提供耐噪声,但由于身体组织的低通波而遭受高频衰减.
- 这种衰减会降低语音信号的质量,需要先进的补偿技术.
研究的目的:
- 使用自主监督学习 (SSL) 开发一个时间域框架,以恢复BC语音信号中的高频信息.
- 为了弥合传感器领域的差距并恢复无参考空气导电信号的光谱扭曲.
主要方法:
- 利用大规模预训练的SSL模型,从BC信号中生成强大的声学先验.
- 集成一个Key-Value Memory模块来检索高保真度的priors.
- 利用门式注意力投射来实现动态融合和高频波回收.
主要成果:
- 拟议的方法显著提高了语音质量,在ABCS和ESMB数据集上分别获得了51%和73%的PESQ收益.
- 与最先进的基线相比,在质量和效率方面都表现出卓越的性能.
- 紧的架构被优化为实际的,现实世界的部署.
结论:
- 基于SSL的框架有效地弥补了BC传感器中硬件引起的缺陷.
- 这种方法为提高BC语音可理解性和质量提供了一个有希望的解决方案.
- 该方法提供了一种高效有效的方法来纠正BC信号中的光谱扭曲.
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