Modeling Piano Interpretation Using Switching Kalman Filter.

An approach of parsing piano music interpretation is presented. We focus mainly on quantifying expressive timing activities. A small number of different expressive timing behaviors (constant, slowing down, speeding up, accent) are defined in order to explain the tempo discretely. Given a MIDI performance of a piano music, we simultaneously estimate both discrete variables that corresponds to the behaviors and continuous variables that describe tempo. A graphical model is introduced to represent the evolution of the discrete behaviors and tempo progression. We demonstrate a computational method that acquires the approximate most likely configuration of the discrete behaviors and the hidden continuous variable tempo. This configuration represent a “smoothed” version of the performance which greatly reduces parametrization while retaining most of its musicality. Experiments are presented on several MIDI piano music performed on a digital piano. An user study is performed to evaluate our method.