Abstract
N-Gram based models have been used for a variety of musical tasks including computer-assisted composition, machine improvisation, music information retrieval, stylistic analysis and cognitive modelling. We present an application-independent evaluation of some recent techniques for improving the performance of a subclass of n-gram models on a range of monophonic music data. 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 that significant and consistent improvements in performance are afforded by several of the evaluated techniques. We discuss the results in terms of previous research carried out in the field of data compression and with natural language and music corpora and conclude by presenting some important directions for future research.
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