ShrutiSense: Microtonal Modeling and Correction in Indian Classical Music

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.