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A Probabilistic Framework for Matching Music Representations

In this paper we introduce a probabilistic framework for matching different music representations (score, MIDI, audio) by incorporating models of how one musical representation might be rendered from another. We propose a dynamical hidden Markov model for the score pointer as a prior, and two observation models, the first based on matching spectrogram data to a trained template, the second detecting damped sinusoids within a frame of audio by subspace methods. The resulting Bayesian framework is robust to local variations in tempo, and can be used for a wide variety of applications. We evaluate both methods in a score alignment context by inferring the posterior distribution of the current position in the score exactly. The spectrogram method is shown to infer the score position reliably with minimal computation, and the damped sinusoid model is able to pinpoint the positions of score events in the audio with a high level of timing accuracy.