Exploring Data Sonification to Enable, Enhance, and Accelerate the Analysis of Big, Noisy, and Multi-Dimensional Data
Exploring Data Sonification to Enable, Enhance, and Accelerate the Analysis of Big, Noisy, and Multi-Dimensional Data
复制标题
探索数据可听化以启用、增强和加速大数据、噪声数据和多维数据的分析
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
B. García
中科院分区：
文献类型：
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作者：
J. Cooke;W. Díaz;G. Foran;J. Hannam;B. García
摘要：我们探索声音和人类声音识别的特性，作为增强和加速纯视觉数据分析方法的手段。这项工作的目的是实现和改进对大数据集、需要快速分析的数据、多维数据以及低信噪比数据中的信号检测的分析。我们提出了一个原型工具 StarSound，用于对天文瞬态光曲线、光谱和功率谱等数据进行声音处理。立体声用于“可视化”和定位所检查的数据，并结合虚拟现实技术讨论 3D 声音，作为提高分析效率和功效的手段，包括快速数据评估和训练机器学习软件。此外，我们探索使用高阶谐波作为同时检查多维数据集的手段。这种方法可以以整体方式解释数据，并有助于发现以前未见过的联系和关系。此外，我们利用人脑的选择性或集中听觉能力，能够在噪声数据中或在类似或更重要的信号中识别所需信号。最后，我们提供了直接受益于数据可听化的研究示例。这里介绍的工作旨在帮助应对即将到来的大数据时代的挑战，并帮助优化、加速和扩展需要人类交互的数据分析方面。
Abstract We explore the properties of sound and human sound recognition as a means to enhance and accelerate visual-only data analysis methods. The aim of this work is to enable and improve the analysis of large data sets, data requiring rapid analysis, multi-dimensional data, and signal detection in data with low signal-to-noise ratio. We present a prototype tool, StarSound, to sonify data such as astronomical transient light curves, spectra, and power spectra. Stereophonic sound is used to ‘visualise’ and localise the data under examination, and 3-D sound is discussed in conjunction with virtual reality technology, as a means to enhance analysis efficiency and efficacy, including rapid data assessment and training machine learning software. In addition, we explore the use of higher-order harmonics as a means to examine simultaneously multi-dimensional data sets. Such an approach can allow the data to be interpreted in a holistic manner and facilitates the discovery of previously unseen connections and relationships. Furthermore, we exploit the capability of the human brain for selective or focused hearing that enables the identification of desired signals in noisy data, or amidst similar or more significant signals. Finally, we provide research examples that benefit directly from data sonification. The work presented here aims to help tackle the challenges of the upcoming era of Big Data and help optimise, speed up and expand aspects of data analysis requiring human interaction.