Stochastic Modeling of a Musical Performance with Expressive Representations from the Musical Score

This paper presents a method for describing the characteristics of human musical performance. We consider the problem of building models that express the ways in which deviations from a strict interpretations of the score occurs in the performance, and that cluster these deviations automatically. The clustering process is performed using expressive representations unambiguously notated on the musical score, without any arbitrariness by the human observer. The result of clustering is obtained as hierarchical tree structures for each deviational factor that occurred during the operation of the instrument. This structure represents an approximation of the performer’s interpretation with information notated on the score they used during the performance. This model represents the conditions that generate the difference in the fluctuation of performance expression and the amounts of deviational factors directly from the data of real performance. Through validations of applying the method to the data measured from real performances, we show that the use of information regarding expressive representation on the musical score enables the efficient estimation of generative-model for the musical performance.