Auditory Representation of Scientific Data

这项工作的长期目标是开发和评估多维数据的听觉表示。对于某些形式的数据(例如，时间序列)，听觉显示可能特别适合于人类感知分析，并且可能比当前的视觉显示有效得多。听觉数据表示本质上是从数据变量到声音事件的属性的映射。在变量是多维的情况下，数据样本的每个维度上的测量确定事件的相应声音属性的值，并且所有测量的值一起确定事件的总体特征。例如，如果事件是一个简单的音调，则一个数据维度可以映射到音调，另一个数据维度映射到响度，第三个数据维度映射到持续时间。根据数据的类型和要进行的分析的类型，可以创建各种听觉显示。由于缺乏合适的计算环境，听觉数据表示的研究受到严重阻碍。首席研究人员正在建立一个原型计算环境，用于研究听觉数据表示法，并利用该环境开发几种不同的听觉数据表示法，并用真实数据和合成数据对这些听觉数据表示法进行初步评估。

The long-term goal of this work is to develop and evaluate auditory representations of multidimensional data. For some forms of data (e.g., time series) auditory displays may be especially well suited to human perceptual analysis and possibly much more effective than current visual displays. Auditory data representation is fundamentally a mapping from a data variable to a property of a sonic event. Where the variable is multidimensional, the measure on each dimension of the data sample determines the value of the corresponding sound property of the event, and the values of all the measures together determine the overall character of the event. For example, if the event is a simple tone, one data dimension could be mapped to pitch, another to loudness, and a third to duration. A variety of auditory displays can be created depending on the kind of data and the kind of analysis to be conducted. Research in auditory data representation has been severely hampered by the lack of an appropriate computing environment. The Principal Investigators are building a prototype computing environment for research in auditory data representation and, using this environment, to develop several different auditory data representations and to conduct preliminary evaluations of these auditory data representations with both real and synthetic data.