PFI-TT: Development of a Novel Audio Technology that Applies Dynamic System Variability to Create Musical Variations for Personal and Public Applications.

创新伙伴关系-技术转化（PFI-TT）项目的更广泛影响/商业潜力是创建一个分析和生成符号序列的系统。符号系统在各种应用中传递信息，如DNA序列、合成生物学和组合化学。它也构成了音乐的主干，它可以被看作是高度依赖上下文的符号序列，其中每个音符或事件预示着即将到来的音符或事件。这项拟议中的技术为音乐流媒体服务的听众和音乐家（占美国人口的26%）提供了与音乐互动的新方式。这项技术将解决音乐行业和许多其他应用。该项目利用非线性混沌系统的可变性来生成与上下文相关的符号序列的变化，例如那些包含音乐作品的符号序列。虽然许多应用需要消除混沌行为的方法，但由于其解对初始条件的敏感性，在其“混沌状态”中运行的耗散系统可以被视为提供了一种自然的可变性机制。这种内置的可变性可以通过混沌映射技术来利用，该技术将有序输入转换为变序输出，其潜在结果范围从与原始工作的高度相似到显著突变。该方法包括将输入解析为原始元素{Ni}的有序序列，这些元素按连续整数值i=1，…，Imax顺序索引。对于每个i，选择算法确定Ni是修改还是替换的候选元素，从而成为“受体元素”。然后，替代或修改算法通过用替代元件改变或替换受体元件对其进行操作。由原始的和被替换的或修改过的元素组成的有序的结果集包括变体输出。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is to create a system that analyzes and generates sequences of symbols. Symbolic systems transmit information in a variety of applications, such as DNA sequences, synthetic biology, and combinatorial chemistry. It also forms the backbone of music, which can be viewed as highly context-dependent sequence of symbols where each note or event foreshadows those yet to come. The proposed technology offers new ways of engaging with music, for both music streaming service listeners and musicians (comprising 26% of the US population) alike. This technology will address the music industry and for many additional applications. The proposed project exploits nonlinear chaotic system variability to generate variations of context-dependent symbol sequences, such as those comprising musical works. While many applications require methods to eliminate chaotic behavior, a dissipative system operating in its `chaotic regime’ can be viewed as offering a natural mechanism for variability, due to the sensitivity of its solutions to initial conditions. This built-in variability can be exploited by a chaotic mapping technique that transforms an ordered input into a variant-ordered output with potential outcomes ranging from high similarity to the original work to significant mutation. The method includes parsing the input into an ordered sequence of original elements {Ni}, which are sequentially indexed by successive integer values i=1,…, Imax. For each i, a selection algorithm determines whether Ni is a candidate for modification or replacement, thus becoming a “receptor element.” A substitution or modifying algorithm then operates on at least one of the receptor elements by varying or replacing it with a substitution element. The resulting ordered set of original and substituted or modified elements comprises the variant output.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.