Sound is a fundamental and rich source of information; playing a key role in many areas from humanities and social sciences through to engineering and mathematics. Sound is more than just data 'signals'. It encapsulates physical, sensorial and emotional, as well as social, cultural and environmental factors. Sound contributes to the transformation of our experiences, environments and beliefs. Sound is all around us and everywhere. Hence, it should come as no surprise that sound is a complex multicomponent entity with a vast assortment of characteristics and applications. Of course, an important question is, what is the best way to store and represent sound digitally to capture these characteristics? What model or method is best for manipulating, extracting and filtering sounds? There are a large number of representations and models, however, one approach that has yet to be used with sound is dual-quaternions. While dual-quaternions have established themselves in many fields of science and computing as an efficient mathematical model for providing an unambiguous, un-cumbersome, computationally effective means of representing multi-component data. Sound is one area that has yet to explore and reap the benefits of dual-quaternions (using sound and audio-related dual-quaternion models). This article aims to explore the exciting potential and possibilities dual-quaternions offer when applied and combined with sound-based models (including but not limited to the applications, tools, machine-learning, statistical and computational sound-related algorithms).

翻译：声音是一种基本且丰富的信息来源；在从人文社会科学到工程与数学的众多领域中扮演着关键角色。声音不仅仅是数据的“信号”。它包含了物理、感官和情感因素，以及社会、文化和环境因素。声音有助于改变我们的体验、环境和信念。声音无处不在，环绕着我们。因此，声音作为一种复杂的多成分实体，拥有各式各样的特征和应用，这一点并不令人惊讶。当然，一个重要的问题是，如何才能以最佳方式数字存储和表示声音，以捕捉这些特征？哪种模型或方法最适合处理、提取和过滤声音？现有的表示方法和模型数量众多，然而，对偶四元数这一方法尚未被应用于声音领域。尽管对偶四元数已在许多科学和计算领域中被确立为一种高效的数学模型，能够以明确、简洁、计算高效的方式表示多成分数据，但声音领域仍未探索并获益于对偶四元数（即使用声音和音频相关的对偶四元数模型）。本文旨在探索对偶四元数在应用于声音模型并结合声音模型（包括但不限于应用、工具、机器学习、统计以及计算声音相关算法）时所展现的激动人心的潜力和可能性。