一种新的方法来估计语音接收值测试的初始声音水平.
Heonzoo Lee1, Rayoung Park1,2, Sejin Kim3
1Department of Intelligent Electronics and Computer Engineering, Chonnam National University, Gwangju, Korea.
概括
一种新的机器学习方法准确地估计了语音接收值 (SRT) 测试的初始声音水平. 这种方法显著减少了测试重复和整体听力评估时间,提高了患者和听力学家的效率.
科学领域:
- 听力学 听力学是指听力学.
- 医疗保健中的机器学习
- 信号处理 信号处理
背景情况:
- 语音接收值 (Speech Reception Threshold,SRT) 是理解50%的语言的最低听力水平.
- 目前的SRT测量涉及重复的刺激调整,导致患者和听力学家疲劳.
- 这种疲劳会损害听力测试结果的可靠性.
研究的目的:
- 为SRT测试开发一个更优的初始声音水平估计.
- 为了减少在SRT测量过程中所需的重复调整的数量.
- 提高听力评估的效率和可靠性.
主要方法:
- 使用一维卷积神经网络 (1D CNN) 实现一种新的机器学习方法.
- 与传统方法相比,1D CNN受过训练,可以预测优越的初始声音水平.
- 拟议的方法旨在估计最初的SRT水平更接近最终确定值.
主要成果:
- 机器学习方法在初始刺激水平和最终SRT之间的差异中实现了37.92%的减少.
- 这表明SRT测试的起点可以更准确地预测.
- 结果表明,与传统方法相比,有很大的改进潜力.
结论:
- 提出的1D CNN方法有效地减少了确定最终SRT所需的重复.
- 这种测试重复次数的减少导致总体听力测试持续时间缩短.
- 这些发现表明,对于听力学评估来说,一种更有效,更可靠的方法可能更为可靠.
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