可解释的机器学习模型用于在男子大学篮球比赛中对人群噪音的反应进行分类
Mitchell C Cutler1, Jason Bickmore1, Mark K Transtrum1
1Department of Physics and Astronomy, Brigham Young University, Provo, Utah 84602, USA.
The Journal of the Acoustical Society of America
|October 24, 2025
概括
机器学习模型可以使用声学特征对篮球比赛中的人群反应进行分类. 功能选择识别关键声音,提高模型准确性和人群情绪分析的可解释性.
科学领域:
- 声学 声学 在声学上
- 机器学习 机器学习
- 运动分析 运动分析
背景情况:
- 在大学篮球比赛中,人群噪音是多种多样的,包括掌声,颂歌和欢呼.
- 对人群声音的声学分析可以为各种应用提供信息,从球员合同到球迷体验和安全.
- 了解人群声学对于开发有效的人群监控和管理系统至关重要.
研究的目的:
- 通过机器学习识别关键的声学特征,以对篮球比赛中的人群反应进行分类.
- 评估特征选择对模型性能和可解释性的影响.
- 评估将短期特征时间历史纳入分类模型的价值.
主要方法:
- 从篮球比赛期间的人群录音中提取了声学特征.
- 机器学习模型,包括随机森林和物流回归,用于分类.
- 应用了特征选择技术,以确定最具预测性的声学特征.
主要成果:
- 与1/3八度乐队形状,音量和音色相关的特征被发现具有高度相关性.
- 结合短期特征时间历史记录,将分类器的准确度提高了高达12%.
- 一些声学特征被证明是未来人群反应的更好的预测者,而不是当前的声音.
结论:
- 特征选择提高了人群反应分类模型的可解释性和性能.
- 声学特征的时间动态对于准确分类人群行为具有重要意义.
- 该研究为实际应用提供了对人群噪声声学特征的见解.
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