arXiv:2607. 06296v1 Announce Type: cross Abstract: This paper presents the design and evaluation of a maintainable hybrid generative architecture for automated music harmony generation from melody.
By Josef Pavlicek
This paper introduces a quantum-inspired computational framework for harmonic decision-making in music. The proposed approach formulates harmonization as an optimization problem within a structured combinatorial space, where multiple candidate chord sequences are evaluated under interacting musical constraints.
arXiv:2607. 05007v1 Announce Type: new Abstract: This paper introduces a quantum-inspired computational framework for harmonic decision-making in music.
By Josef Pavl\'i\v{c}ek, Petra Pavl\'i\v{c}kov\'a, Martin Molhanec
arXiv:2607. 24873v1 Announce Type: new Abstract: Recent advances in AI music generation have enabled users to create complete musical pieces from natural-language prompts.
By Callie C. Liao, Duoduo Liao, Ellie L. Zhang
Recent advances in AI music generation have enabled users to create complete musical pieces from natural-language prompts. However, most existing systems follow a prompt-and-regenerate paradigm, making iterative refinement difficult because users must repeatedly recreate compositions instead of directly evolving existing musical ideas.
We study generative modeling of Bach-style symbolic piano music using a shared MIDI corpus and three model families: autoregressive LSTMs with attention, latent-variable models including recurrent VAEs and vector-quantized VAEs, and generative adversarial networks. We compare their ability to model polyphonic note sequences, learn useful latent representations, and generate stylistically coherent compositions.