by Dr. Michael Rhoades and ChatGPT
Initialization:
This research project is a collaboration between the ChatGPT Large Language Model (LLM) and Dr. Michael Rhoades. The first of several long-term goals is to develop a closed, adaptive Csound compositional system that generates original Csound scores based solely on constraints, rules, and aesthetic direction defined by Rhoades.
From the outset, we established that the system must not derive from existing Csound scores or external musical sources. Instead, it should evolve strictly through iterative dialogue, creating a feedback loop between Rhoades and ChatGPT’s internal generative logic and adaptive behavior.
Foundational Framework:
We defined a workflow wherein Csound generates musical material using Rhoades’ supplied Csound orchestra, tendency masks, and parameter constraints. All quasi-randomness, determinism, and transformational strategies operate entirely within this closed environment.
The system will evolve by adapting its use of frequency sets, waveform distributions, densities, envelopes, and spectral features based on Rhoades’ evaluations of each output.
Analytical Layer:
To deepen the system’s ability to respond to Rhoades’ compositional sensibilities, we incorporated spectral analysis using Csound’s native tools (for example, pvanal, lpread, and related utilities). These allow us to extract time-varying spectral descriptors, envelope shapes, energy distributions, and harmonic partial trajectories, providing more detailed internal representations of each generated sound file.
This analytical data becomes part of the feedback loop, enabling the generative engine to refine both structural and timbral behaviors in response to Rhoades’ feedback.
Feedback & Cataloging:
A key component of the project is the systematic cataloging of Rhoades’ responses and ratings, alongside ChatGPT’s analyses of each generated sound file. We are establishing a structured schema (rating, descriptive notes, timbral preferences, structural comments, and so on) so the system can interpret the feedback in a form suitable for adaptation.
Once Rhoades provides compiled responses to the first set of generated sound files, they will be integrated into a training-style dataset that guides ChatGPT in adjusting its generative compositional strategies—still entirely within the closed ruleset.
Adaptive Evolution:
The long-term trajectory of the project is to create a system that increasingly reflects Rhoades’ individual compositional sensibilities. Subsequent iterations of score generation will be informed by prior outputs, his evaluations, and the internal analytical data.
Over time, the system will refine its mapping functions, probabilistic selections, and structural choices to develop a musical language aligned with a collaboratively shaped artistic voice—while remaining strictly within the closed constraints initially defined.
Current Status:
The long-term trajectory of the project is to create a system that increasingly reflects Rhoades’ individual compositional sensibilities. Subsequent iterations of score generation will be informed by prior outputs, his evaluations, and the internal analytical data.
Over time, the system will refine its mapping functions, probabilistic selections, and structural choices to develop a musical language aligned with a collaboratively shaped artistic voice—while remaining strictly within the closed constraints initially defined.
There will be more to come as this collaboration progresses.