Quantitative Analysis of Animal Vocal Signals

这个项目将继续发展生物声学信号的计算机分析。许多动物利用声音进行交流，对一些非人类物种的声音学习的研究为理解人类的语言发展提供了模型。尽管这很重要，但很少有严格的、标准化的方法来描述生物声学信号，或定量评估和比较动物的发声。现在使用的技术种类繁多，其中许多非常费力，难以重复，而且往往依赖于主观解释。这项工作将进一步发展定量分析动物发声的计算机辅助方法。这项研究有两个相关的方面。1)构建先进的、标准化的信号处理技术，作为描述、存储和比较这些声音的工具。这些技术将为实验室分析交流声音的听觉感知提供定量基础，或为各种动物的声音交流提供实地数据。2)这些技术将被用来检查一个被充分研究的鸣禽的声音发展，它学会了特定的歌声。由于新的解剖学和神经化学信息，鸣禽的声乐学习最近得到了更好的理解。人们已经勾勒出大脑中参与歌曲产生和听觉的部分之间的特定途径，并发现了特定的激素和神经化学物质会影响歌曲的产生和感知。这些技巧将使歌曲的复杂声音在学习过程中随着歌曲的各个组成部分的发展而得到分析。这种方法有望为研究学习过程中复杂的发声行为提供一种新颖而富有成效的方法，并将对言语和交流以及听觉感知和生物声学领域产生影响。

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.