English(EN) From Individual Songs to Music Corpora: Evaluating Explainable Harmonic Decision-Making on HookTheory

可解释AI模型在音乐和声决策制定方面得到评估

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员评估了一个可解释的和声决策模型，即可解释旋律和声器（EMH），其处理音乐语料库的能力。该研究将EMH应用于HookTheory的摘录，根据明确的音乐标准生成和弦序列，并与人类标注进行比较。虽然精确和弦一致性约为20%，但和声功能一致性达到47%以上，表明该模型在基于语料库的音乐决策制定评估方面具有潜力。 AI

影响展示了一种在音乐生成等创意领域评估AI模型的方法。

排序理由关于AI模型在音乐和声方面评估的学术论文。

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arXiv cs.AI TIER_1 English(EN) · Josef Pavl\'i\v{c}ek, Martin Pelik\'an · 2026-10-19 04:00

从单曲到音乐语料库：评估 HookTheory 上可解释的乐句和声决策

arXiv:2610.22416v1 Announce Type: cross Abstract: Automatic melody harmonization is often evaluated through individual compositions or data-driven corpus models. This study examines whether an explainable harmonic decision model developed for individual songs can be applied to in…

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从单曲到音乐语料库：评估 HookTheory 上可解释的乐句和声决策

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