用离散扩散模型根据旋律谱生成更符合和弦的钢琴伴奏
D3PIA: A Discrete Denoising Diffusion Model for Piano Accompaniment Generation From Lead sheet
- 基于钢琴滚筒表示，通过邻域注意力对齐旋律与伴奏
- 在POP909数据集上更忠实保持和弦结构，主观听感更连贯
- 适合需要精确和声控制的音乐生成任务
在符号音乐领域中，从旋律谱生成钢琴伴奏是一项挑战性任务，需根据给定的旋律和和弦约束（如旋律谱所示）生成完整的钢琴乐曲。本文提出一种基于离散扩散的钢琴伴奏生成模型D3PIA，利用钢琴滚筒表示中旋律与伴奏的局部对齐特性。D3PIA引入邻域注意力（NA），同时编码旋律谱并将其作为条件以预测钢琴伴奏中的音符状态。该设计通过高效关注附近的旋律和和弦信息，增强局部上下文建模能力。我们在广泛使用的基准数据集POP909上评估模型。客观评价结果显示，与连续扩散模型及Transformer基线相比，D3PIA能更忠实保留和弦条件；此外，主观听觉测试表明，D3PIA生成的伴奏在音乐连贯性上优于对比模型。
原文摘要 · Abstract (English)
Generating piano accompaniments in the symbolic music domain is a challenging task that requires producing a complete piece of piano music from given melody and chord constraints, such as those provided by a lead sheet. In this paper, we propose a discrete diffusion-based piano accompaniment generation model, D3PIA, leveraging local alignment between lead sheet and accompaniment in piano-roll representation. D3PIA incorporates Neighborhood Attention (NA) to both encode the lead sheet and condition it for predicting note states in the piano accompaniment. This design enhances local contextual modeling by efficiently attending to nearby melody and chord conditions. We evaluate our model using the POP909 dataset, a widely used benchmark for piano accompaniment generation. Objective evaluation results demonstrate that D3PIA preserves chord conditions more faithfully compared to continuous diffusion-based and Transformer-based baselines. Furthermore, a subjective listening test indicates that D3PIA generates more musically coherent accompaniments than the comparison models.
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