基于不同数据集的声音景观维度建模方法的比较)
Siegbert Versümer1,2, Patrick Blättermann1, Fabian Rosenthal1
1Institute of Sound and Vibration Engineering, University of Applied Sciences Düsseldorf, Münsterstr. 156, Düsseldorf 40476, Germany.
The Journal of the Acoustical Society of America
|January 15, 2025
概括
对声景研究的统计模型进行比较,发现非线性方法,如随机森林,在预测声景方面表现出色. 混合效果模型为各种音景数据提供了概括.
科学领域:
- 环境声学环境声学
- 精神声学是一种精神声学.
- 统计建模 统计建模
背景情况:
- 声音景观研究面临的挑战是由于不同的方法和指标.
- 评估统计建模技术在音景研究中的适用性是很困难的.
研究的目的:
- 为了比较五种统计方法和两个性能指标来建模声音景观的愉悦性和事件性.
- 在不同的音景数据集和预测变量中评估模型性能.
主要方法:
- 应用线性和非线性回归,包括机器学习方法 (随机森林,极端梯度增强),对三个音景数据集.
- 利用了7个声学和3个社会人口预测指标.
- 采用固定效应和混合效应模型,使用非样本R2.2评估绩效.
主要成果:
- 模型通常在预测事件性方面表现更好,而不是愉快性.
- 非线性方法的表现始终优于线性回归,非线性技术的表现也相当.
- 混合效应模型产生了更普遍的预测,但在未见的群体上性能略低.
结论:
- 非线性机器学习方法对于音景建模,特别是对于事件性更优越.
- 对于小型,不平衡的音景数据集,建议使用专业分割进行交叉验证.
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