* External authors




VaryNote: A Method to Automatically Vary the Number of Notes in Symbolic Music

Juan M. Huerta*

Bo Liu*

Peter Stone

* External authors

CMMR 2023



Automatically varying the number of notes in symbolic music has various applications in assisting music creators to embellish simple tunes or to reduce complex music to its core idea. In this paper, we formulate the problem of varying the number of notes while preserving the essence of the original music. Our method, VaryNote, adopts an autoencoder architecture in combination with a masking mechanism to control the number of notes. To train the weights of the pitch autoencoder we present a novel surrogate divergence, combining the loss of pitch reconstructions with chord predictions end-to-end. We evaluate our results by plotting chord recognition accuracy with increasing and decreasing numbers of notes, analyzing absolute and relative musical features with a probabilistic framework, and by conducting human surveys. The human survey results indicate humans prefer VaryNote output (with 1.5, 1.9 times notes) over the original music, suggesting that it can be a useful tool in music generation applications.

Related Publications

Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning

AAAI, 2024
Zizhao Wang*, Caroline Wang*, Xuesu Xiao*, Yuke Zhu*, Peter Stone

Two desiderata of reinforcement learning (RL) algorithms are the ability to learn from relatively little experience and the ability to learn policies that generalize to a range of problem specifications. In factored state spaces, one approach towards achieving both goals is …

Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents

AAAI, 2024
Arrasy Rahman*, Jiaxun Cui*, Peter Stone

Robustly cooperating with unseen agents and human partners presents significant challenges due to the diverse cooperative conventions these partners may adopt. Existing Ad Hoc Teamwork (AHT) methods address this challenge by training an agent with a population of diverse tea…

Learning Optimal Advantage from Preferences and Mistaking it for Reward

AAAI, 2024
W. Bradley Knox*, Stephane Hatgis-Kessell*, Sigurdur Orn Adalgeirsson*, Serena Booth*, Anca Dragan*, Peter Stone, Scott Niekum*

We consider algorithms for learning reward functions from human preferences over pairs of trajectory segments---as used in reinforcement learning from human feedback (RLHF)---including those used to fine tune ChatGPT and other contemporary language models. Most recent work o…

  • HOME
  • Publications
  • VaryNote: A Method to Automatically Vary the Number of Notes in Symbolic Music


Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.