HarmonyRFG: A Rule-Guided Spiking Transformer Framework for Real-Time Chord Progression Generation
DOI:
https://doi.org/10.54097/ktcjx318Keywords:
Music generation; chord progression; Spiking Neural Network; Transformer; Markov chain; generative art; interactive music.Abstract
This paper presents HarmonyRFG, a rule-guided chord generation framework that connects established harmonic practice with deep-learning-based sequence modeling. The framework couples the long-range representation capacity of the Transformer with the event-driven temporal modeling of Spiking Neural Networks, so that chord identity, harmonic function, and duration can be learned as interdependent musical variables rather than isolated symbols. Markov transition constraints and harmony-based scoring are further introduced to regulate local chord movement while retaining generative flexibility. The proposed approach therefore supports chord sequences that are musically coherent, responsive to real-time control, and suitable for creative contexts such as interactive installations, ambient composition, human-computer co-creation, and therapeutic sound environments.
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