The research involves modeling human aspects of musical performance. Like speech, the exquisite precision of trained performance and mastery of an instrument does not lead to an exactly repeatable performed musical surface with respect to note timings and other parameters. The goal is to achieve sufficient modeling capabilities to predict some aspects of expression in performance of a score. The present approach attempts to capture the variety of ways a particular passage might be played by a single individual, so that a predicted performance can be defined from within a closed sphere of possibilities characteristic of that individual. Ultimately, artificial realizations might be produced by chaining together different combinations at the level of the musical phrase, or guiding in real time a synthetic or predicted performance.
A pianist was asked to make recordings (in the Disklavier MIDI data format) from a progression of rehearsals during preparation of a work (by Charles Ives) for concert. The samples include repetitions of the excerpt from the same day as well as recordings over a period of months. This performance data (containing timing and velocity information) was analyzed using classical statistical feature extraction methods tuned to classify the variety of realizations. Chunks of data representing musical phrases were segmented from the recordings according to an ``effort parameter'' that has been previously described. Presently under study is a simulation system stocked with a comprehensive set of distinct musical interpretations which permits the model to create artificial performances. It is possible that such a system could eventually be guided in real time by a pianist's playing, such that the system is predicting ahead of an unfolding performance. Possible applications would include present performance situations in which appreciable electronic delay (on the order of 100's of msec.) is musically problematic.
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