Development of Sonification Design Theory: Metaphors, Mappings, Holistic Sound Design, and Data Specific Sonification

听觉表示的数据，或发音，承诺收益的时间和高维数据的表示，在数据监控任务的眼睛是忙碌，在任务中需要识别的模式，在数据集，在高压力或临界条件下，跨模态相关性将是有价值的，和显示用户是学生或研究人员与视觉障碍。 然而，很少有工作已经完成，以确定最好的方式来映射数据维度，如温度或压力，到听觉显示维度，如音高或克里思。 我们对声音所产生的隐喻性联想也知之甚少，也不知道如何利用这些内涵来设计有效的、令人信服的、令人愉快的、易于学习和直观使用的发音。 本研究旨在发现用于科学发声的最佳数据显示映射，并调查这些最佳映射是否在应用领域内和/或跨应用领域变化。 为了实现这一目标，PI和他的团队将开发一个跨平台的研究环境，其中包括复杂的声音合成和数据收集功能。 一系列实验将研究： (a)数据到声音映射中的听众偏好和人群定型; (b)不同科学领域的映射如何不同;以及（c）数据声音化对图形解释和数据挖掘的有效性。 PI将开发跨平台发声软件工具，以创建适合研究领域、用户特征和待探索数据类型的听觉显示。 这一工具将促进多个学科的数据分析，并为视力残疾的科学家和学生提供直接进入科学世界的机会。

Auditory representation of data, or sonification, promises gains in the representation of temporal and high-dimensional data, in data monitoring tasks where the eyes are busy, in tasks requiring recognition of patterns in a data set, in high-stress or critical conditions where cross-modal correlations would be of value, and where the display users are students or researchers with visual disabilities. However, very little work has been done to determine the best ways to map data dimensions, such as temperature or pressure, onto auditory display dimensions, such as pitch or tempo. We also know little about the metaphorical associations that sounds create, and how to utilize these connotations to design effective, compelling, and pleasant sonifications which are easy to learn and intuitive to use. This research seeks to discover the optimal data-to-display mappings for use in scientific sonification, and to investigate whether these optimal mappings vary within and/or across fields of application. To accomplish this, the PI and his team will develop a cross-platform research environment that incorporates sophisticated sound synthesis and data collection capabilities. A series of experiments will investigate: (a) listener preferences and population stereotypes in data-to-sound mappings; (b) how mappings differ across various scientific areas; and (c) the effectiveness of data sonification for graphical interpretations and data mining. The PI will develop a cross-platform sonification software tool to create auditory displays suitable for the domain of study, the characteristics of the user, and the type of data to be explored. This tool will facilitate data analysis in a number of disciplines, and provide visually-disabled scientists and students immediate inroads into the world of science.