对于大陆范围的环境噪声水平的地理空间模型的特征选择
Katrina Pedersen1, Mark K Transtrum1, Kent L Gee1
1Physics and Astronomy, Brigham Young University, Provo, Utah 84602, USA.
The Journal of the Acoustical Society of America
|August 23, 2023
概括
减少环境声音建模的地理空间特征是可能的,但结果需要仔细解释. 需要对先进的维度减小进行进一步的研究,以便在有限的数据基础上进行准确的声学预测.
科学领域:
- 环境声学环境声学
- 地理空间建模的使用方法
- 机器学习 机器学习
背景情况:
- 大陆规模的环境声音建模是具有挑战性的,因为不同的环境和有限的声学训练数据.
- 之前的工作使用了51个地理空间层和496个声学站点的机器学习来预测连续美国的声音水平.
研究的目的:
- 详细说明在环境声音建模中使用的地理空间特征下调选择过程.
- 进一步研究声学预测模型的维度减小技术.
- 评估特征选择对模型预测误差和稳定性的影响.
主要方法:
- 描述了基于数据质量和特征之间的相关性而进行的地理空间特征下调选择.
- 在现有的51个地理空间层中应用了四种不同的特征选择方法.
- 采用了交叉验证 (leave-one-out) 中位数绝对偏差的交叉验证来评估模型性能.
主要成果:
- 地理空间特征可能会被减少到15个,而不会显著降低预测错误.
- 总体预测表明特征选择结果对问题制定特征的敏感性.
- 模型性能各不相同,这表明在解释特征选择结果时需要谨慎.
结论:
- 特性选择可以减少环境声音模型中的维度,但结果值得怀疑.
- 复杂的尺寸缩小技术对于处理有限的培训数据和分发轮班至关重要.
- 需要进一步的研究,以开发可靠的方法,以稀疏的数据进行声学建模.
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