# Controlling Surprisal in Music Generation via Information Content Curve Matching

arXiv: https://arxiv.org/abs/2408.06022 · HTML: https://mlsanity.com/paper/2408.06022/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://mlsanity.com/paper/2107.14795/) (2021)
- [Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders](https://mlsanity.com/paper/1704.01279/) (2017)
- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [GiantMIDI-Piano: A large-scale MIDI dataset for classical piano music](https://mlsanity.com/paper/2010.07061/) (2020)
- [AI Song Contest: Human-AI Co-Creation in Songwriting](https://mlsanity.com/paper/2010.05388/) (2020)
- [The Piano Inpainting Application](https://mlsanity.com/paper/2107.05944/) (2021)
- [The Batik-plays-Mozart Corpus: Linking Performance to Score to Musicological Annotations](https://mlsanity.com/paper/2309.02399/) (2023)
- [Exploring Sampling Techniques for Generating Melodies with a Transformer Language Model](https://mlsanity.com/paper/2308.09454/) (2023)

## Built on by (most cited first, 7 of 7)

- [Surprise! Uniform Information Density Isn't the Whole Story: Predicting Surprisal Contours in Long-form Discourse](https://mlsanity.com/paper/2410.16062/) (2024)
- [Estimating Musical Surprisal in Audio](https://mlsanity.com/paper/2501.07474/) (2025)
- [Estimating Musical Surprisal from Audio in Autoregressive Diffusion Model Noise Spaces](https://mlsanity.com/paper/2508.05306/) (2025)
- [Stemphonic: All-at-once Flexible Multi-stem Music Generation](https://mlsanity.com/paper/2602.09891/) (2026)
- [Predicting States of Understanding in Explanatory Interactions Using Cognitive Load-Related Linguistic Cues](https://mlsanity.com/paper/2603.20079/) (2026)
- [Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders](https://mlsanity.com/paper/2511.05350/) (2025)
- [Explicit Tonal Tension Conditioning via Dual-Level Beam Search for Symbolic Music Generation](https://mlsanity.com/paper/2511.19342/) (2025)

## Abstract (arXiv)

In recent years, the quality and public interest in music generation systems have grown, encouraging research into various ways to control these systems. We propose a novel method for controlling surprisal in music generation using sequence models. To achieve this goal, we define a metric called Instantaneous Information Content (IIC). The IIC serves as a proxy function for the perceived musical surprisal (as estimated from a probabilistic model) and can be calculated at any point within a music piece. This enables the comparison of surprisal across different musical content even if the musical events occur in irregular time intervals. We use beam search to generate musical material whose IIC curve closely approximates a given target IIC. We experimentally show that the IIC correlates with harmonic and rhythmic complexity and note density. The correlation decreases with the length of the musical context used for estimating the IIC. Finally, we conduct a qualitative user study to test if human listeners can identify the IIC curves that have been used as targets when generating the respective musical material. We provide code for creating IIC interpolations and IIC visualizations on https://github.com/muthissar/iic.