PhraseVAE 和 PhraseLDM：用于全曲多轨符号音乐生成的潜在扩散
PhraseVAE and PhraseLDM: Latent Diffusion for Full-Song Multitrack Symbolic Music Generation
AI总结：
PhraseVAE 和 PhraseLDM 提出了一种用于全曲多轨符号音乐生成的潜在扩散框架，通过短语级潜在表示实现高效生成和长序列建模。
摘要：
本文报告了一种新的全曲符号音乐生成范式。现有的符号模型操作于音符属性标记，存在序列过长、上下文长度有限以及对长距离结构支持弱的问题。我们通过引入 PhraseVAE 和 PhraseLDM，首个针对全曲多轨符号音乐的潜在扩散框架，解决这些问题。PhraseVAE 将任意变量长度的多音部音符序列压缩为一个单一的紧凑 64 维短语级潜在表示，具有高重建保真度，允许形成良好的潜在空间和高效的生成建模。基于此潜在空间，PhraseLDM 生成完整的多轨歌曲，无需任何自回归组件。该系统消除了小节级顺序建模，支持多达 128 小节的音乐（8 分钟，64 BPM），并生成具有连贯局部纹理、惯用乐器模式和清晰全局结构的完整歌曲。仅使用 4500 万参数，我们的框架在几秒内生成完整歌曲，同时保持竞争性的音乐质量和生成多样性。这些结果表明，短语级潜在扩散为符号音乐生成中的长序列建模提供了一种有效且可扩展的解决方案。我们希望这项工作能鼓励未来的符号音乐研究超越音符属性标记，考虑短语级单元作为更有效且更具音乐意义的建模目标。
英文摘要：
This technical report presents a new paradigm for full-song symbolic music generation. Existing symbolic models operate on note-attribute tokens and suffer from extremely long sequences, limited context length, and weak support for long-range structure. We address these issues by introducing PhraseVAE and PhraseLDM, the first latent diffusion framework designed for full-song multitrack symbolic music. PhraseVAE compresses an arbitrary variable-length polyphonic note sequence into a single compact 64-dimensional phrase-level latent representation with high reconstruction fidelity, allowing a well-structured latent space and efficient generative modeling. Built on this latent space, PhraseLDM generates an entire multi-track song in a single pass without any autoregressive components. The system eliminates bar-wise sequential modeling, supports up to 128 bars of music (8 minutes at 64 bpm), and produces complete songs with coherent local texture, idiomatic instrument patterns, and clear global structure. With only 45M parameters, our framework generates a full song within seconds while maintaining competitive musical quality and generation diversity. Together, these results show that phrase-level latent diffusion provides an effective and scalable solution to long-sequence modeling in symbolic music generation. We hope this work encourages future symbolic music research to move beyond note-attribute tokens and to consider phrase-level units as a more effective and musically meaningful modeling target.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
