Quantitative Analysis of Animal Vocal Signals

该项目将继续发展计算机化的 生物声学信号分析。 许多动物利用声音 交流，并研究一些非人类的声音学习 物种提供了理解语言发展的模型， 人类 尽管如此重要，很少有严格的， 用于描述生物声学信号的标准化方法，或用于 定量评估和比较动物的发声。 一 现在使用各种各样的技术，其中许多非常 费力，难以重复，并且通常依赖于 主观解释。 这项工作将进一步发展计算机辅助方法， 定量分析动物发声。 这项研究是 在两个相关的方面进行设计。 1)先进的标准化信号 处理技术将被构建，作为工具， 对这些声音的描述、存储和比较。 这些 技术将为实验室提供定量基础 分析通信声音的听觉感知，或 关于各种动物声音交流的实地数据。（二） 这些技术将被用来检查声乐发展中的一个 善于学习特定歌曲的鸣禽。 声乐 最近人们对鸣禽的学习有了更好的了解 因为新的解剖学和神经化学信息。 的 大脑各部分之间的特定通路已经被勾勒出来 参与了歌曲的产生和听觉， 和神经化学物质会影响 对歌曲的感知。 这些技术将使复杂的 歌曲的声音被分析为个人 歌曲的成分在学习过程中发展。 这种方法有望提供一种新的生产方式， 研究学习过程中复杂的发声行为， 对语言和交流领域产生了影响， 听觉感知和生物声学。

This project will continue the development of computerized analysis of bioacoustic signals. Many animals use sound for communicating, and research on vocal learning in some non-human species provides models for understanding speech development in humans. Despite this importance, there are few rigorous, standardized methods for describing bioacoustic signals, or for quantitatively assessing and comparing animal vocalizations. A wide variety of techniques are used now, and many are very laborious, difficult to repeat, and often depend on subjective interpretation. This work will further develop computer-aided methods for quantitatively analyzing animal vocalizations. The research is designed on two related fronts. 1) Advanced, standardized signal processing techniques will be constructed, to serve as tools for the description, storage and comparison of such sounds. These techniques will provide a quantitative base for laboratory analysis of auditory perception of communication sounds, or for field data on vocal communication in a wide range of animals. 2) These techniques will be used to examine vocal development in a well-studied songbird that learns its specific song. Vocal learning in songbirds has become better understood recently because of new anatomical and neurochemical information. The specific pathways have been outlined between parts of the brain involved in song production and hearing, and specific hormones and neurochemicals have been found to affect the production and perceptions of the song. These techniques will allow the complex vocal sounds of the song to be analyzed as the individual components of the song develop during learning. This approach promises to provide a novel productive way of investigating complex vocal behavior during learning, and will have impact on the fields of speech and communication as well as auditory perception and bioacoustics.