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Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models
April 1999 (vol. 21 no. 4)
pp. 360-370

Abstract—In this paper, we address an important step toward our goal of automatic musical accompaniment—the segmentation problem. Given a score to a piece of monophonic music and a sampled recording of a performance of that score, we attempt to segment the data into a sequence of contiguous regions corresponding to the notes and rests in the score. Within the framework of a hidden Markov model, we model our prior knowledge, perform unsupervised learning of the data model parameters, and compute the segmentation that globally minimizes the posterior expected number of segmentation errors. We also show how to produce "on-line" 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.

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Index Terms:
Automatic musical accompaniment, hidden Markov models, computer music.
Citation:
Christopher Raphael, "Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 360-370, April 1999, doi:10.1109/34.761266
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