让机器理解印度古典音乐的22个微音程，修复走调旋律并补全缺失音符。
ShrutiSense: Microtonal Modeling and Correction in Indian Classical Music
- 用22音程框架的有限状态机修正走调旋律，符合印度音乐语法规则。
- 在0.2至0.4噪声水平下，纠错准确率达86.7%-90.0%，最高达91.3%。
- 适合研究南亚音乐、跨文化音乐生成与微音程建模的学者和开发者。
印度古典音乐依赖于22个微音程（shrutis）构成的复杂音高体系，赋予其超越12平均律的表达张力。现有符号化音乐处理工具无法体现这些微音程差异及决定旋律走向的文化特定拉加（raga）语法规则。我们提出ShrutiSense，一套专为印度古典音乐设计的符号化音高处理系统，解决两大关键任务：(1) 修正西式化或失真的音高序列；(2) 补全缺失音符的旋律片段。方法上采用互补模型：基于22音程框架的音高感知有限状态转换器（FST）实现上下文修正，结合拉加特有转移规则的语法约束隐马尔可夫模型（GC-SHMM）完成上下文补全。在五个拉加的模拟数据上评估显示，ShrutiSense（FST模型）在纠错任务中达到91.3%的音程分类准确率，噪声水平0.2至0.4时准确率为86.7%-90.0%。系统对音高噪声容忍度达±50音分，各拉加间表现稳定（90.7%-91.8%），有效保持印度古典音乐的文化真实性。
原文摘要 · Abstract (English)
Indian classical music relies on a sophisticated microtonal system of 22 shrutis (pitch intervals), which provides expressive nuance beyond the 12-tone equal temperament system. Existing symbolic music processing tools fail to account for these microtonal distinctions and culturally specific raga grammars that govern melodic movement. We present ShrutiSense, a comprehensive symbolic pitch processing system designed for Indian classical music, addressing two critical tasks: (1) correcting westernized or corrupted pitch sequences, and (2) completing melodic sequences with missing values. Our approach employs complementary models for different tasks: a Shruti-aware finite-state transducer (FST) that performs contextual corrections within the 22-shruti framework and a grammar-constrained Shruti hidden Markov model (GC-SHMM) that incorporates raga-specific transition rules for contextual completions. Comprehensive evaluation on simulated data across five ragas demonstrates that ShrutiSense (FST model) achieves 91.3% shruti classification accuracy for correction tasks, with example sequences showing 86.7-90.0% accuracy at corruption levels of 0.2 to 0.4. The system exhibits robust performance under pitch noise up to +/-50 cents, maintaining consistent accuracy across ragas (90.7-91.8%), thus preserving the cultural authenticity of Indian classical music expression.
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