Authors - Satyendra Sharma, Pradeep Laxkar Abstract - Reconstructing polyphonic musical sequences represents a significant challenge in computational music analysis. This study presents a method based on empirical entropy and the analysis of multi-voice bigrams to identify and re-construct missing notes in polyphonic sequences. The approach combines statistical modeling of transitions between simultaneous voices in a musical piece, represented as tuples duration:interval|duration:interval|... depending on the number of voices, with techniques for generating and ranking possible segments according to probability and entropy. Results show that considering multi-voice bigrams effectively captures the polyphonic structure and improves the accuracy of missing note prediction. This work opens new perspectives for the application of probabilistic models to polyphonic music and AI-assisted music generation.
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