# Exploring Conditioning for Generative Music Systems with Human-Interpretable Controls

arXiv: https://arxiv.org/abs/1907.04352 · HTML: https://mlsanity.com/paper/1907.04352/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

## Built on by (most cited first, 8 of 8)

- [Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion](https://mlsanity.com/paper/2402.14285/) (2024)
- [GTR-CTRL: Instrument and Genre Conditioning for Guitar-Focused Music Generation with Transformers](https://mlsanity.com/paper/2302.05393/) (2023)
- [ProgGP: From GuitarPro Tablature Neural Generation To Progressive Metal Production](https://mlsanity.com/paper/2307.05328/) (2023)
- [Between the AI and Me: Analysing Listeners' Perspectives on AI- and Human-Composed Progressive Metal Music](https://mlsanity.com/paper/2407.21615/) (2024)
- [From Words to Music: A Study of Subword Tokenization Techniques in Symbolic Music Generation](https://mlsanity.com/paper/2304.08953/) (2023)
- [Musical Speech: A Transformer-based Composition Tool](https://mlsanity.com/paper/2108.01043/) (2021)
- [Efficient Fine-Grained Guidance for Diffusion Model Based Symbolic Music Generation](https://mlsanity.com/paper/2410.08435/) (2024)
- [MIDI-Draw: Sketching to Control Melody Generation](https://mlsanity.com/paper/2305.11605/) (2023)

## Abstract (arXiv)

Performance RNN is a machine-learning system designed primarily for the generation of solo piano performances using an event-based (rather than audio) representation. More specifically, Performance RNN is a long short-term memory (LSTM) based recurrent neural network that models polyphonic music with expressive timing and dynamics (Oore et al., 2018). The neural network uses a simple language model based on the Musical Instrument Digital Interface (MIDI) file format. Performance RNN is trained on the e-Piano Junior Competition Dataset (International Piano e-Competition, 2018), a collection of solo piano performances by expert pianists. As an artistic tool, one of the limitations of the original model has been the lack of useable controls. The standard form of Performance RNN can generate interesting pieces, but little control is provided over what specifically is generated. This paper explores a set of conditioning-based controls used to influence the generation process.