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马来西亚热带雨林声景中的时间模式使用声学指数和深层嵌入训练在一天的时间估计证明)

Yen Yi Loo¹, Mei Yi Lee², Samien Shaheed³

¹Sunway Centre for Planetary Health, Sunway University, Petaling Jaya, Selangor 47500, Malaysia.

The Journal of the Acoustical Society of America

|January 6, 2025

概括

生态声学为监测热带生态系统提供了一个解决方案. 用深度神经网络方法在声音环境中估计一天中的时间,其性能与传统方法相美.

科学领域:

生态生态学生态生态学
生物声学是一种生物声学.
机器学习机器学习

背景情况:

城市发展威胁到热带生态系统,需要有效的长期监测.
生态声学提供了一种非侵入性的生态评估方法.
分析大型声学数据集需要有效的方法来克服物种识别挑战.

研究的目的:

用传统的声学指数和深度声学嵌入来分析时间声景变化.
为了比较这些方法的预测准确度,用于一天中的时间估计.
在不同的生态环境中评估它们在监督分类和无监督聚类中的表现.

主要方法:

利用深度神经网络训练,从声学录音中估计一天中的时间.
采用传统的生态声学指数和深度生态声学嵌入用于声音景观分析.
用于日间时间预测,分类和聚类的比较方法性能.

主要成果:

无论是传统的声学指数还是深度声学嵌入,都表现出了相似的性能.
深度学习方法显示了分析时间声景动态的潜力.
性能因记录地点,息地类型和季节而异.

结论:

深度生态声学嵌入为声音景观分析提供了可行的替代方案.
进一步完善深度学习方法可以加强生态监测.
这种方法有助于理解热带生态系统的时空音景变化.

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