Pianoroll-Event: A Novel Score Representation for Symbolic Music
Lekai Qian, Haoyu Gu, Dehan Li, Boyu Cao, and Qi Liu

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…
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Taxonomy
TopicsMusic and Audio Processing · Music Technology and Sound Studies · Neuroscience and Music Perception
