🤖 AI Summary
This work addresses the limitations of existing symbolic music representations, where grid-based encodings suffer from low efficiency and data sparsity, while pure event sequences struggle to preserve structural invariance and spatial locality. To overcome these issues, the authors propose a novel hybrid representation that integrates piano-roll and event-sequence paradigms through four complementary event types: Frame, Gap, Pattern, and Musical Structure. This design effectively captures temporal dependencies, local spatial features, and higher-level structural information. The resulting representation achieves a superior trade-off between sequence length and vocabulary size, yielding a 1.36–7.16× improvement in encoding efficiency. Experiments using autoregressive generative models demonstrate that the proposed method significantly outperforms state-of-the-art baselines in both automatic metrics and human evaluations.
📝 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.