走向声音景观的跨学科正式化
Mikel D Jedrusiak1, Thomas Harweg1, Timo Haselhoff2
1Department of Computer Science, Computergraphics, Technical University of Dortmund, Otto-Hahn-Straße 16, 44227 Dortmund, Germany.
The Journal of the Acoustical Society of America
|April 10, 2024
概括
这项研究引入了一种新的,跨学科的模型来分析声音景观,使不同研究领域的沟通更好. 调查结果显示,声音景观可以被分类,但并不总是按预定义的土地使用类别进行分类.
科学领域:
- 声学生态学 声学生态学
- 环境科学环境科学
- 数据科学是数据科学.
背景情况:
- 音景研究跨越多个学科,导致各种术语和定义.
- 这种异质性使跨学科沟通和数据比较复杂化.
- 需要采用统一的方法来整合各种音景数据.
研究的目的:
- 开发一种独立于特定学科概念的声音景观分析的正式化.
- 创建一个分层模型,能够处理异质的音景数据结构.
- 根据声音景观特征来对土地使用类型进行分类.
主要方法:
- 一个分层模型,包含时间依赖的声音源和地理数据.
- 使用传感器功能来表示声学组成.
- 使用3D卷积神经网络和频率相关矩阵,分析了来自23个地点的音景录音.
主要成果:
- 展示了将声音景观分组为不同的类别的能力.
- 发现分类的音景组不一定与预定义的土地使用类别保持一致.
- 通过案例研究验证了正式化的应用.
结论:
- 拟议的正式化为跨学科的音景研究提供了一个统一的框架.
- 声音景观的分类是可行的,但土地使用并不总是一个直接的决定因素.
- 该模型有助于在不同领域更一致地理解和比较音景数据.
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