音乐音频生成模型综述教程：覆盖扩散、流匹配与评估方法
A Tutorial on Generative Models for Music Audio
想做音乐生成的同学看这篇：把音频编解码器、扩散、流匹配这些方法用统一框架讲透，还带系统对比表和听测评估指南。
这篇 arXiv 教程面向熟悉神经网络训练的读者，系统讲解音乐音频生成方法，涵盖神经音频编解码器、自回归与掩码 token 模型、扩散和流匹配等范式。文中用统一记号串联训练目标与采样流程，并附对比表记录各系统的表示、方法、任务和发布信息。应用部分将器乐生成、带人声歌曲、音频编辑和实时合奏的音乐需求对应到模型设计上。教程还讲解如何解读自动指标、设计听测实验，以及评估数据来源、记忆化、署名和输出检测的证据，并选取相关法律争议案例说明训练与发布的合规影响。
A Tutorial on Generative Models for Music Audio
Music audio generation spans instrumental clips, full songs with vocals, edits of existing recordings, and systems that play alongside a musician in real time. This monograph explains the methods behind these systems for readers familiar with neural-network training who are new to music generation. It introduces neural audio codecs and autoencoders, autoregressive and masked token models, diffusion and flow matching, sampling acceleration, and preference optimization in a shared notation. Worked examples connect training objectives and sampling procedures to published systems. The application sections relate the musical requirements of instrumental generation, songs, editing, and live performance to model design. Comparison tables record representations, methods, inputs, tasks, and release information documented in papers and official sources. Symbolic composition, speech, and sound effects are included where they connect to music audio. We explain how to interpret automatic metrics and learned judges, design and report listening tests, and assess evidence about data provenance, memorization, attribution, and output detection. Selected legal decisions and disputes illustrate the implications for training and release. The reviewed systems combine established generative paradigms with new representations, planning stages, and post-training procedures. Differences in data, scale, and evaluation limit what cross-system comparisons can establish about the sources of reported gains.
