Automatic segmentation of acoustic musical signals using hidden Markov models

In this paper, we address an important step toward our goal of automatic mu sical accompaniment-the segmentation problem. Given a score to a piece of m onophonic music and a sampled recording of a performance of that score, we attempt to segment the data into a sequence of contiguous regions correspon ding to the notes and rests in the score. Within the framework of a hidden Markov model, we model our prior knowledge, perform unsupervised learning o f the data model parameters, and compute the segmentation that globally min imizes the posterior expected number of segmentation errors. We also show h ow to produce "online" estimates of score position. We present examples of our experimental results, and readers are encouraged to access actual sound data we have made available from these experiments.