可解读的声道和呼吸系统逆转通过物理知情的神经操作员
Mengtao Deng1, Cheng Liu2, Zhangmei Yang3
1Teacher Training College, Dazhou Vocational and Technical College, Dazhou, 635000, Sichuan, China. y05011817@163.com.
Scientific reports
|March 1, 2026
概括
这项研究引入了基于物理的神经网络框架,用于准确的声道建模和实时分析. 这种新方法提高了音色忠实度,并为个性化的语音应用提供了可解释的生理洞察力.
科学领域:
- 声学和信号处理
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 声道和呼吸系统的生理变异挑战了准确的音色建模和实时声分析.
- 目前的数据驱动方法往往缺乏物理解释性和扬声器稳定性.
- 从声学数据中准确地重建声道几何和呼吸系统动态,对于高级的声音分析至关重要.
研究的目的:
- 提出一个基于物理的多式逆转框架,使用Kolmogorov-Arnold (KAN) 运算符来解释声道几何和呼吸系统动态的重建.
- 在语音分析中实现高音色保真和低延迟.
- 为细粒度音色重建和个性化声乐分析提供基础.
主要方法:
- 一个嵌套的三层KAN将音频频谱逆转为声道横截面区域.
- 一个封闭的递归模块使用质量-动量保存来限制压力演变.
- 优化了超分辨率预测头,使用分数顺序的时间规范化和波方程余值,提高了音色保真度.
主要成果:
- 实现了1.83 ± 0.32 dB的日志光谱扭曲和6.4 ± 1.1% (1.2-2.4 kHz) 的子频段误差率.
- 在边缘设备上展示了低端到端延迟 (14.2-18.3毫秒) 和紧的内存使用量 (108-121 MB).
- 在未见的语音类型中保持最小的声道几何误差 (MAE-CSA ≤0.23) 和呼吸系统估计偏差 (RMSE-P ≤0.52).
结论:
- 将神经操作员与物理约束相结合,可以准确,可解释和实时地逆转声体生理.
- 拟议的框架为先进的钢格重建提供了一个原则性的技术基础.
- 这种方法为个性化语音分析和改进的语音技术铺平了道路.
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