The present study assesses two types of models for melodic complexity: one based on expectancy violations and the other one related to an information-theoretic account of redundancy in music. Seven different datasets spanning artificial sequences, folk and pop songs were used to refine and assess the models. The refinement eliminated unnecessary components from both types of models. The final analysis pitted three variants of the two model types against each other and could explain from 46-74% of the variance in the ratings across the datasets. The most parsimonious models were identified with an information-theoretic criterion. This suggested that the simplified expectancy-violation models were the most efficient for these sets of data. However, the differences between all optimised models were subtle in terms both of performance and simplicity.
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