来自室内体育赛事的人群噪音的数据驱动分解
Mitchell C Cutler1, Mylan R Cook1, Mark K Transtrum1
1Department of Physics and Astronomy, Brigham Young University, Provo, Utah 84602, USA.
The Journal of the Acoustical Society of America
|February 11, 2024
概括
这项研究开发了一种机器学习方法,将不同声音 (如人群噪音和音乐) 与体育赛事音频分开. 该技术成功地识别和分类复杂录音中的各种声源.
科学领域:
- 声学 声学 在声学方面
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 体育赛事录音包含复杂的声学混合物,使得很难隔离特定的声音来源,如人群噪音.
- 现有的方法在这些环境中难以有效地区分人群声音,音乐,个人声音和公共讲话 (PA) 系统.
研究的目的:
- 介绍一种数据驱动的分解方法,用于从体育赛事录音中分离不同的声源.
- 分析不同体育,人群大小和场馆的大学体育赛事中声音水平的光谱特征.
主要方法:
- 利用机器学习技术,特别是主要组件分析 (PCA),对30个大学体育赛事的光谱图.
- 分析了87.5%的光谱变化,使用三个主要组成部分,无论体育,场地或人群组成.
- 应用高斯混合模型集群到三维元件系数表示,以识别不同的声源集群.
主要成果:
- 确定了三个主要的光谱形状,可以有效地分离各种声源.
- 发现三个主要组成部分可以代表87.5%的音频信号的光谱变化.
- 使用高斯混合模型发现了九个不同的集群,该集群可听力地分离不同组合的声源.
结论:
- 基于PCA和高斯混合模型的拟议数据驱动分解方法,可以在体育赛事录音中成功分离复杂的声源.
- 这种方法提供了一种强大的方法来区分人群噪音,音乐,个人声音和音响系统.
- 这些发现为分析和理解直播体育赛事的声学景观提供了有价值的工具.
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