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• The framework aims to provide an interpretable, distributional analysis of motivic transformation patterns.technical 95%
• A probabilistic framework is developed for modeling motif transformations in symbolic music.method 95%
• The proposed framework is applied to Beethoven's piano sonatas.method 95%
• Multiple datasets providing melodic, rhythmic, harmonic, and motivic information are integrated into a unified analytical representation.method 95%
• Motif transformations are represented as multilabel variables by comparing each motif instance to a designated reference occurrence within its local context.method 95%
• A multilabel Conditional Random Field (CRF) is introduced to model the influence of motif-level musical features on transformation occurrence and co-occurrence of transformation families.method 95%
• The proposed framework supports quantitative investigation of musical structure and complexity in symbolic corpora.technical 95%
• The unary model provides a better fit than the baseline model, indicating that motif-level features contribute explanatory power beyond baseline transformations.result 90%

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