# Musical Form Generation

arXiv: https://arxiv.org/abs/2310.19842 · HTML: https://mlsanity.com/paper/2310.19842/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 6 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [Is Conditional Generative Modeling all you need for Decision-Making?](https://mlsanity.com/paper/2211.15657/) (2022)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [Masked Autoencoders that Listen](https://mlsanity.com/paper/2207.06405/) (2022)

## Built on by (most cited first, 1 of 1)

- [Explicit Tonal Tension Conditioning via Dual-Level Beam Search for Symbolic Music Generation](https://mlsanity.com/paper/2511.19342/) (2025)

## Abstract (arXiv)

While recent generative models can produce engaging music, their utility is limited. The variation in the music is often left to chance, resulting in compositions that lack structure. Pieces extending beyond a minute can become incoherent or repetitive. This paper introduces an approach for generating structured, arbitrarily long musical pieces. Central to this approach is the creation of musical segments using a conditional generative model, with transitions between these segments. The generation of prompts that determine the high-level composition is distinct from the creation of finer, lower-level details. A large language model is then used to suggest the musical form.