缓慢而稳定:用于区分动物发声的听觉特征
Ronald W Di Tullio1,2, Linran Wei3, Vijay Balasubramanian1,2,4
1David Rittenhouse Laboratory, Department of Physics and Astronomy, University of Pennsylvania, USA.
bioRxiv : the preprint server for biology
|July 15, 2024
概括
听众可以通过识别时间规律来区分动物声音,这是光谱-时间相关性的平稳变化. 这项研究表明,最可预测的声音组件是跨物种准确声声区分的关键.
科学领域:
- 生物声学是一种生物声学.
- 听觉感知是一种听觉感知.
- 机器学习 机器学习
背景情况:
- 动物的发声包含复杂的光谱时间信息.
- 听众可以利用时间规律来进行声音歧视.
研究的目的:
- 调查声调的时间规律是否有助于物种和个人歧视.
- 识别负责声调识别的关键声学特征.
主要方法:
- 利用缓慢特征分析 (SFA) 来从发音中提取暂时规律的特征.
- 包括来自鸟类,人类和 rhesus macaques 的发音.
- 雇佣了一名经过培训的分类器来测试基于学习的特征的听觉歧视.
主要成果:
- 在所有测试群体中实现了优异的发音歧视 (>95%).
- 歧视表现主要是由大约10个具有高度时间规律的特征驱动的.
- 确定这些常规特征与可预测的声音组件有很强的相关性.
结论:
- 时间规律对于对动物发声的听觉歧视至关重要.
- 一小组暂时常规的特征有效地捕获了用于声音识别的基本信息.
- 这些发现支持这样一个假设,即可预测的声学模式是语音处理的基础.
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