Abstract
The behavior ofa human accompanist is simulated using a hidden Markov model. The model is divided in two levels. The lower level models directly the incoming signal, without requiring analysis techniques that are prone to errors; the higher level models the performance, taking into account all the possible errors made by the musician. Alignment is performed through a decoding technique alternative to classic Viterbi decoding. A novel technique for the training is also proposed. After the performance has been aligned with the score, the information is used to compute local tempo and drive the automatic accomaniment.
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© 2001 Springer-Verlag Berlin Heidelberg
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Orio, N. (2001). An Automatic Accompanist Based on Hidden Markov Models. In: Esposito, F. (eds) AI*IA 2001: Advances in Artificial Intelligence. AI*IA 2001. Lecture Notes in Computer Science(), vol 2175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45411-X_8
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DOI: https://doi.org/10.1007/3-540-45411-X_8
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