Mathematical Foundations of Polyphonic Music Generation via Structural Inductive Bias
This work addresses a domain-specific problem in AI music generation, offering incremental improvements with verifiable mathematical insights.
The paper tackled the 'Missing Middle' problem in polyphonic music generation by introducing structural inductive bias, achieving a 48.30% reduction in parameters and a 9.47% reduction in validation loss.
This monograph introduces a novel approach to polyphonic music generation by addressing the "Missing Middle" problem through structural inductive bias. Focusing on Beethoven's piano sonatas as a case study, we empirically verify the independence of pitch and hand attributes using normalized mutual information (NMI=0.167) and propose the Smart Embedding architecture, achieving a 48.30% reduction in parameters. We provide rigorous mathematical proofs using information theory (negligible loss bounded at 0.153 bits), Rademacher complexity (28.09% tighter generalization bound), and category theory to demonstrate improved stability and generalization. Empirical results show a 9.47% reduction in validation loss, confirmed by SVD analysis and an expert listening study (N=53). This dual theoretical and applied framework bridges gaps in AI music generation, offering verifiable insights for mathematically grounded deep learning.