用VQ-VAE学习韩国民乐的连续音高模式，无需标注即可捕捉核心音乐特征。
Pitch Contour Tokenization using VQ-VAE and Its Application on Korean Traditional Music Analysis
- 通过VQ-VAE从无标注音频中自动学习局部音高轮廓的离散表示。
- 在韩国传统音乐上，模型学习到的符号可无监督还原专家定义的四声类别。
- 适用于以音高轮廓为核心的音乐分析，适合民族音乐学与跨文化研究者。
音乐的计算分析常依赖离散表示，但许多音乐传统以连续音高运动为核心，难以分割为音符类单元。本文提出一种方法，使用VQ-VAE从无标注音频中直接学习局部音高轮廓模式的词汇表，将固定长度的轮廓片段量化为有限码本。为使学习到的符号对分段位置、时间微调和音域变化具有稳定性，训练时采用在一组候选时移与音域变换中最佳对齐下的重构目标。该方法应用于韩国民间唱曲（Pansori），学习到的符号能无监督地恢复专家定义的四声类别（sigimsae），且个体符号与主要两种调式（Gyemyeonjo与Ujo）高度对应，支持其作为轮廓主导传统音乐的语料级分析单位。
原文摘要 · Abstract (English)
Computational analysis of music often relies on discrete representations, yet many musical traditions are organized around continuous pitch movement that resists segmentation into note-like units. For such traditions, the discrete units that analysis would build on are not given in advance. We address this gap by learning a vocabulary of local pitch-contour patterns directly from unlabeled audio, using a VQ-VAE that quantizes fixed-length contour segments into a finite codebook. To make the learned tokens stable across segmentation positions and small variations in timing and pitch range, we train the model with a reconstruction objective evaluated under the best alignment among a set of candidate temporal and pitch-domain transformations. Applied to Korean traditional music, the learned tokens recover information about expert-defined sigimsae categories without supervision, and in pansori individual tokens align with the two principal modes, Gyemyeonjo and Ujo, supporting their use as units for corpus-level analysis of contour-centric traditions.
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