基于机器学习的声压水平的预测来自病态和健康的语音信号
Manila Kodali1, Sudarsana Reddy Kadiri2, Shrikanth Narayanan2
1Department of Information and Communications Engineering, Aalto University, Espoo 02150, Finland.
The Journal of the Acoustical Society of America
|March 13, 2025
概括
这项研究引入了机器学习,从未校准的录音中估计声声压力水平 (SPL). 开发的系统实现了准确的估计,这对于现实世界的语音分析至关重要.
科学领域:
- 语音处理 语音处理
- 机器学习是机器学习.
- 声学 声学 在声学方面
背景情况:
- 用音压水平 (SPL) 衡量的声音强度对于语音分析至关重要.
- 传统的SPL测量需要校准的设备,限制了现实世界的应用.
- 从非校准记录中估计SPL是一个重大挑战.
研究的目的:
- 调查机器学习 (ML) 的可行性,以从非校准语音录音中估计声声压力水平 (SPL).
- 开发和评估基于ML的系统,以在各种语言条件下准确估计SPL.
主要方法:
- 开发了几个基于ML的系统,包括特征提取和回归阶段.
- 比较了传统的声学特征,预训练的特征和组合的特征集.
- 评估了三种不同的回归模型来估计SPL.
主要成果:
- 最好的ML系统在SPL估计中达到约2dB的平均绝对误差.
- 在病态 (心力衰竭) 和健康语音数据上验证了表现.
- 该研究证明了ML在克服校准限制方面的有效性.
结论:
- 基于ML的估计为在未校准的现实场景中测量语音SPL提供了可行的解决方案.
- 精确的SPL估计是可以实现的,即使是正常化的振幅语音信号.
- 这种方法在临床诊断和语音研究中具有潜在的应用.
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