A Stochastic Temporal Model of Polyphonic MIDI Performance with Ornaments
This addresses the challenge of score-performance matching in music information processing, particularly for complex performances with ornaments, but it is incremental as it builds on existing stochastic models.
The authors tackled the problem of modeling indeterminacies in ornament realization for polyphonic MIDI performance, proposing a hidden Markov model that incorporates temporal information and yields highly accurate matching for performances with many ornaments and errors.
We study indeterminacies in realization of ornaments and how they can be incorporated in a stochastic performance model applicable for music information processing such as score-performance matching. We point out the importance of temporal information, and propose a hidden Markov model which describes it explicitly and represents ornaments with several state types. Following a review of the indeterminacies, they are carefully incorporated into the model through its topology and parameters, and the state construction for quite general polyphonic scores is explained in detail. By analyzing piano performance data, we find significant overlaps in inter-onset-interval distributions of chordal notes, ornaments, and inter-chord events, and the data is used to determine details of the model. The model is applied for score following and offline score-performance matching, yielding highly accurate matching for performances with many ornaments and relatively frequent errors, repeats, and skips.