🤖 AI Summary
研究通过训练一个基于作曲家身份条件的Transformer模型，学习到了一个连续的作曲风格嵌入空间，并发现该空间能够捕捉到音乐历史结构。
📝 Abstract
We analyze the composer embeddings learned by a composer-conditioned Transformer as a continuous latent space of compositional style, rather than merely as an internal representation for generation. A model that recursively predicts melody continuations was trained on melodic sequences extracted from MIDI data, conditioned on composer identity (124 composers). Principal component analysis of the learned composer embedding matrix (124x128) shows that the first principal component correlates strongly with composer birth year (r = -0.884, p < 0.001, n = 123), a stronger correlation than we obtain by applying the same PC1-birth-year analysis to existing general-purpose audio-text embeddings (CLAP, MuQ-MuLan) trained on unrelated audio-text corpora, not on symbolic melody generation. A shuffle test (2,000 permutations) confirms that the Silhouette score for stylistic-period labels is statistically significant (0.0110, p < 0.001). We further show that vector arithmetic in the embedding space captures meaningful stylistic relationships between composers. These results suggest that composer embeddings, learned without supervision beyond composer identity, form an interpretable latent space that captures musical-historical structure.