用卷积神经网络检测乐谱中的段落边界，效果优于音频方法。
Barwise Section Boundary Detection in Symbolic Music Using Convolutional Neural Networks
- 设计新编码方式将乐器信息转为三通道钢琴谱
- 在6134首乐谱上实现0.77的F1分数
- 适合音乐结构分析、自动作曲研究者使用
当前音乐结构分析（MSA）主要针对音频数据。虽然符号化乐谱可转为音频并用现有方法分析，但无法利用其清晰表达的音高、节奏和配器信息。本文研究符号化音乐中的段落边界检测问题——判断某时间点是否为音乐段落的转换点。首先，我们构建了一个人工标注的MIDI数据集，从Lakh MIDI数据集中手工筛选6134个文件并添加元数据。其次，训练深度学习模型，在固定长度的音乐窗口内分类是否存在段落边界。数据表示采用基于合成泛音的新编码方案，将任意乐器配置映射为三通道钢琴谱。模型在该任务上取得0.77的F1分数，相比基于音频的监督学习方法提升0.22，相比无监督块匹配分割（CBM）音频方法提升0.31。本文公开数据集、代码与模型。
原文摘要 · Abstract (English)
Current methods for Music Structure Analysis (MSA) focus primarily on audio data. While symbolic music can be synthesized into audio and analyzed using existing MSA techniques, such an approach does not exploit symbolic music's rich explicit representation of pitch, timing, and instrumentation. A key subproblem of MSA is section boundary detection-determining whether a given point in time marks the transition between musical sections. In this paper, we study automatic section boundary detection for symbolic music. First, we introduce a human-annotated MIDI dataset for section boundary detection, consisting of metadata from 6134 MIDI files that we manually curated from the Lakh MIDI dataset. Second, we train a deep learning model to classify the presence of section boundaries within a fixed-length musical window. Our data representation involves a novel encoding scheme based on synthesized overtones to encode arbitrary MIDI instrumentations into 3-channel piano rolls. Our model achieves an F1 score of 0.77, improving over the analogous audio-based supervised learning approach and the unsupervised block-matching segmentation (CBM) audio approach by 0.22 and 0.31, respectively. We release our dataset, code, and models.
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