用文化相关的混音增强提升波斯乐器识别准确率
Persian Musical Instruments Classification Using Polyphonic Data Augmentation
- 基于单音样本生成符合波斯音乐特点的多声部混合数据
- 在真实波斯音乐上达到0.795的ROC-AUC最佳表现
- 适合对非西方音乐和文化敏感的智能音乐系统研究者
乐器分类对音乐信息检索（MIR）和生成音乐系统至关重要，但针对非西方传统音乐如波斯音乐的研究仍不足。本文提出一个新数据集，包含七种传统波斯乐器、两种常见但非波斯起源的乐器（小提琴、钢琴）及人声的独立录音。我们设计了一种文化相关的数据增强策略，从单音样本生成真实的多声部混合音频。采用带有分类头的MERT模型（Music undERstanding with large-scale self-supervised Training），在通过手动标注传统歌曲片段获得的分布外数据上进行评估。在真实波斯多声部音乐上，该方法取得最高ROC-AUC值0.795，凸显音高与时间一致性的互补优势。结果表明，文化根基的数据增强可有效提升波斯乐器识别鲁棒性，为文化包容性音乐信息检索与多样化音乐生成系统奠定基础。
原文摘要 · Abstract (English)
Musical instrument classification is essential for music information retrieval (MIR) and generative music systems. However, research on non-Western traditions, particularly Persian music, remains limited. We address this gap by introducing a new dataset of isolated recordings covering seven traditional Persian instruments, two common but originally non-Persian instruments (i.e., violin, piano), and vocals. We propose a culturally informed data augmentation strategy that generates realistic polyphonic mixtures from monophonic samples. Using the MERT model (Music undERstanding with large-scale self-supervised Training) with a classification head, we evaluate our approach with out-of-distribution data which was obtained by manually labeling segments of traditional songs. On real-world polyphonic Persian music, the proposed method yielded the best ROC-AUC (0.795), highlighting complementary benefits of tonal and temporal coherence. These results demonstrate the effectiveness of culturally grounded augmentation for robust Persian instrument recognition and provide a foundation for culturally inclusive MIR and diverse music generation systems.
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