做音乐AI或生成式模型的研究者，这篇论文直接对比了三大主流方法在巴赫风格音乐上的效果，结论清晰，适合作为技术选型参考。
该研究系统比较了三种生成模型在巴赫风格钢琴音乐生成上的表现：带注意力的自回归LSTM、潜变量模型（包括循环VAE和向量量化VAE）以及生成对抗网络。实验表明，带注意力的自回归LSTM生成的音乐连贯性最佳；向量量化有助于缓解后验崩溃问题，生成的结构化输出优于传统循环VAE；对抗方法能捕捉局部音高模式，但训练困难且风格泛化不稳定。研究揭示了不同方法在符号音乐生成中的优势与局限。
Generative Modeling of Bach-Style Symbolic Music: A Comparative Study of Autoregressive, Latent-Variable, and Adversarial Approaches
We study generative modeling of Bach-style symbolic piano music using a shared MIDI corpus and three model families: autoregressive LSTMs with attention, latent-variable models including recurrent VAEs and vector-quantized VAEs, and generative adversarial networks. We compare their ability to model polyphonic note sequences, learn useful latent representations, and generate stylistically coherent compositions. Our experiments show that the autoregressive LSTM with attention produces the most musically coherent samples, while vector quantization helps mitigate posterior collapse and yields more structured outputs than conventional recurrent VAEs. The adversarial approach captures local pitch patterns but remains difficult to train and generalizes less reliably to Bach's style. These results highlight the relative strengths and failure modes of autoregressive, latent-variable, and adversarial approaches for symbolic music generation.