Classification of Vocal Fatigue Using sEMG: Data Imbalance, Normalization, and the Role of Vocal Fatigue Index Scores
Classification of Vocal Fatigue Using sEMG: Data Imbalance, Normalization, and the Role of Vocal Fatigue Index Scores
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使用 sEMG 对声音疲劳进行分类：数据不平衡、标准化和声音疲劳指数评分的作用
DOI：
10.3390/app11104335
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
G. DeSouza
中科院分区：
文献类型：
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作者：
Yixiang Gao;Maria Dietrich;G. DeSouza
我们之前的研究表明，通过对从受试者前颈部获得的表面肌电信号的模式识别，可以对模拟受压和实际声带疲劳的声音产生与声带健康的声音产生进行分类。在这些研究中，普遍接受的声音疲劳指数因子1 （VFI-1）被用于正常和声音疲劳的声音制作的基础真值标记。通过最近的实验，研究了其他可能影响分类的因素，如表面肌电信号归一化和数据不平衡，即。声音健康的受试者和声音疲劳的受试者之间的差异很大。因此，在本文中，我们提出了一种改进的分类方法，该方法来源于对这些外部因素对声乐疲劳分类的影响的广泛研究。该研究对88名声音健康和疲劳的受试者（包括学生教师和教师）的大量肌电信号进行了研究，并得出了关于如何优化机器学习方法以早期检测声音疲劳的重要结论。
Our previous studies demonstrated that it is possible to perform the classification of both simulated pressed and actual vocally fatigued voice productions versus vocally healthy productions through the pattern recognition of sEMG signals obtained from subjects’ anterior neck. In these studies, the commonly accepted Vocal Fatigue Index factor 1 (VFI-1) was used for the ground-truth labeling of normal versus vocally fatigued voice productions. Through recent experiments, other factors with potential effects on classification were also studied, such as sEMG signal normalization, and data imbalance—i.e., the large difference between the number of vocally healthy subjects and of those with vocal fatigue. Therefore, in this paper, we present a much improved classification method derived from an extensive study of the effects of such extrinsic factors on the classification of vocal fatigue. The study was performed on a large number of sEMG signals from 88 vocally healthy and fatigued subjects including student teachers and teachers and it led to important conclusions on how to optimize a machine learning approach for the early detection of vocal fatigue.