![]() This thesis provides several contributions. An exhaustive evaluation of S(S(S)) was carried out with expert rock musicians which demonstrated that the system shows an overall high level of creativity and that the single agents of the system are indeed capable of modifying their compositional style. S(S(S)) aims to provide an indirect proof of the validity of GC and MST. The core concepts of GC and MST are used as a basis to design S(S(S)), an AI multi-agent system which has the double goal of creating convincing rock songs and of simulating a society of virtual songwriters which create songs, listen to the songs composed by their peers, and change their compositional style based on what they hear. By using systems theory, MST attempts to describe how single agents and musical societies evolve over time. These notions are at the basis of MST, a theory which aims to describe how agents (both biological and non-biological) involved in musical activities interact with each other and create musical societies. In doing so, GC provides a new definition of a creative system as well as of an iterative creative process. ![]() GC attempts to extend the notion of creativity to non-biological systems. Two formalised frameworks are proposed, i.e., General Creativity (GC) and Music Systems Theory (MST). To respond to the research questions a double approach based both on theoretical investigation and computer simulation is employed. The research questions of this work are: How can we formally define a creative system? How can we formalise the behaviour of creative music agents? How can we simulate the behaviour of creative music agents computationally? Neither mathematical nor computational systems sophisticated enough exist that attempt to describe/simulate how music agents behave and form musical societies. ![]() And again, lacked that critical engagement, although provided a neat understanding of the math/theory behind sound.This thesis focuses on the analysis and simulation of the behaviour of cognitive systems – both biological and non-biological – which are able to process musical information (i.e., creative music agents). I tried reading through Synth Secrets, but just found too much knowledge specific to hardware synths. Perhaps because it’s focus is on fundamentally understanding the basics, and not doing more advanced stuff. Syntorial was useful as well, because of that critical aspect, but hasn’t been as useful as going through a synth. I guess in art terms, it’s a bit like tracing a drawing vs actually trying to draw what you see. You’re not having to actively engage with the material, you’re merely recreating it. ![]() ![]() I guess because although they’re showing you how to make a sound, you’re not critically learning anything. In the past, I’ve tried stuff like watching YouTube tutorials on how to make a sound, but I didn’t find them effective from a learning perspective. Personally, what’s helping me most is to go through a synth (in my case, Sylenth1) and just go through each preset, try and describe what I hear, then try and recreate that sound in the same synth from scratch based on what I hear. I’ve been getting into sound design and wanted to understand what helped you personally develop those skills □ ![]()
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