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.
