深度音频嵌入用于语音集群的声音集群
Paul Best1, Sébastien Paris1, Hervé Glotin1
1Université de Toulon, Aix Marseille Univ, CNRS, LIS, Toulon, France.
PloS one
|July 10, 2023
概括
这项研究引入了一种新的深度学习方法,用于分析动物的发声,自动化声乐谱的表征. 该方法使用自动编码器来创建更好的表示,提高生物声学研究的准确性和效率.
科学领域:
- 生物声学是一种生物声学.
- 机器学习 机器学习
- 动物沟通动物沟通
背景情况:
- 非人类动物的沟通分析通常涉及将声序手动转录成离散单元,形成特定物种的声谱.
- 专家驱动的声乐谱表征可能耗时且容易产生偏见,需要自动化解决方案.
研究的目的:
- 开发和评估一种新的机器学习方法,用于在非人类动物中自动化声乐谱表征.
- 通过使用深度表示学习来提高分析动物发声的效率和客观性.
主要方法:
- 使用一个卷积式自动编码器网络来学习动物发声的抽象表示.
- 从6种物种 (鸟类和海洋哺乳动物) 的8个数据集中对专家标记的发声类型进行了评估.
- 将自动编码器方法与最先进的语音表示和集群方法进行比较.
主要成果:
- 自动编码方法显著提高了声声表现的相关性,以表达剧集的特征.
- 与现有方法相比,在各种数据集和物种中表现出卓越的性能.
- 这种方法需要最小的参数调整才能有效应用.
结论:
- 使用自动编码器进行深度表示学习为生物声学分析和声乐谱表征提供了强大而高效的工具.
- 开发的方法减少了与传统分析技术相关的偏见和劳动.
- 发布了一个Python包,以促进生物声学研究的自动编码器的使用,以帮助 vocal repertoire 浏览和注释.
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