Improving Predictions of Derived Viewpoints in Multiple Viewpoints Systems

This paper presents and tests a method for improving the predictive power of derived viewpoints in multiple viewpoints systems. Multiple viewpoint systems are a well established method for the statistical modelling of sequential symbolic musical data. A useful class of viewpoints known as derived viewpoints map symbols from a basic event space to a viewpoint-specific domain. Probability estimates are calculated in the derived viewpoint domain before an inverse function maps back to the basic event space to complete the model. Since an element in the derived viewpoint domain can potentially map onto multiple basic elements, probability mass is distributed between the basic elements with a uniform distribution. As an alternative, this paper proposes a distribution weighted by zero-order frequencies of the basic elements to inform this probability mapping. Results show this improves the predictive performance for certain derived viewpoints, allowing them to be selected in viewpoint selection.