Pianoroll-Event: A Novel Score Representation for Symbolic Music

TL;DR

Pianoroll-Event introduces a new event-based encoding scheme for symbolic music that balances sequence length and vocabulary size, improving efficiency and model performance over existing methods.

Contribution

The paper presents Pianoroll-Event, a novel event-based representation combining structural and efficiency benefits for symbolic music encoding.

Findings

Encoding efficiency improved by up to 7.16 times

Models using Pianoroll-Event outperform baselines in evaluations

Effective balance between sequence length and vocabulary size achieved

Abstract

Symbolic music representation is a fundamental challenge in computational musicology. While grid-based representations effectively preserve pitch-time spatial correspondence, their inherent data sparsity leads to low encoding efficiency. Discrete-event representations achieve compact encoding but fail to adequately capture structural invariance and spatial locality. To address these complementary limitations, we propose Pianoroll-Event, a novel encoding scheme that describes pianoroll representations through events, combining structural properties with encoding efficiency while maintaining temporal dependencies and local spatial patterns. Specifically, we design four complementary event types: Frame Events for temporal boundaries, Gap Events for sparse regions, Pattern Events for note patterns, and Musical Structure Events for musical metadata. Pianoroll-Event strikes an effective balance between sequence length and vocabulary size, improving encoding efficiency by 1.36\times to 7.16\times over representative discrete sequence methods. Experiments across multiple autoregressive architectures show models using our representation consistently outperform baselines in both quantitative and human evaluations.