N-gram models have been employed for a number of musical tasks including the development of practical applications providing computational support for creative individuals as well as theoretical studies of creative processes. Our goal in this research is to evaluate, in an application independent manner, some recent techniques for improving the performance on monophonic music of a subclass of such models based on the Prediction by Partial Match (PPM) algorithm. These techniques include the use of escape method C, interpolated smoothing and unbounded orders. We have applied these techniques incrementally to eight melodic datasets using cross entropy computed by 10-fold cross-validation on each dataset as our performance metric. The results demonstrate statistically significant performance improvements afforded by the use of all three techniques. We discuss these findings in terms of previous research carried out in the field of data compression and with natural language and music corpora and present some directions for future research. It is our hope that these improvements may be applied usefully to specific musical tasks.
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