← 研踪 PaperTrailarXiv:2509.04899 · v5
Encoding of musical structures in hidden units of restricted Boltzmann machines
首次提交 2025-09-05 16:18
版本更新 2026-11-06 18:02
雷达观测 2026-11-09 11:19
摘要
Restricted Boltzmann machines (RBMs) are energy-based models originating from statistical physics. In this study, we investigate how musical structures are represented in the hidden layer of a Bernoulli-Bernoulli RBM trained on binary piano-roll representations of J.~S.~Bach's music. By examining the visible patterns induced by individual hidden units, we find that the hidden layer primarily captures local temporal and pitch-statistical features rather than explicit musical concepts such as melodies, chords, or keys. Free-energy comparisons show that the trained RBM assigns higher probabilities to the examined musical inputs than to digit or noise patterns. Analysis using t-SNE, cosine similarity, and PCA reveals that hidden representations are sensitive to absolute pitch and input overlap, with transposed versions of the same piece not necessarily mapped close together. Generated samples exhibit local pitch organization but limited long-range coherence. These findings illustrate how a simple statistical-physics model captures certain regularities in structured musical data while highlighting its limitations in representing higher-level musical relationships.
作者备注：21 pages, 11 figures, manuscript was revised
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v5 · 2026-11-06 18:02 · 最新观测版本
Encoding of musical structures in hidden units of restricted Boltzmann machines
Restricted Boltzmann machines (RBMs) are energy-based models originating from statistical physics. In this study, we investigate how musical structures are represented in the hidden layer of a Bernoulli-Bernoulli RBM trained on binary piano-roll representations of J.~S.~Bach's music. By examining the visible patterns induced by individual hidden units, we find that the hidden layer primarily captures local temporal and pitch-statistical features rather than explicit musical concepts such as melodies, chords, or keys. Free-energy comparisons show that the trained RBM assigns higher probabilities to the examined musical inputs than to digit or noise patterns. Analysis using t-SNE, cosine similarity, and PCA reveals that hidden representations are sensitive to absolute pitch and input overlap, with transposed versions of the same piece not necessarily mapped close together. Generated samples exhibit local pitch organization but limited long-range coherence. These findings illustrate how a simple statistical-physics model captures certain regularities in structured musical data while highlighting its limitations in representing higher-level musical relationships.
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