The information content of each successive note in a piece of music is not an intrinsic musical property but depends on the listener's own model of a genre of music. Human listeners' models can be elicited by having them guess successive notes and assign probabilities to their guesses by gambling. While previous research has concentrated on the overall information content of whole pieces of music, the present study evaluates and compares the two kinds of model in fine detail. Apart from the intrinsic interest of comparing human with computational models of music, several conclusions are drawn for the improvement of computational models. As graduate programs in Computer Science grow and mature and undergraduate populations stabilize, an increasing proportion of our resources is being devoted to the training of researchers in the field.
Many inefficiencies are evident in our graduate programs.
These include undesirably long average times to thesis completion, students' poor work habits and general lack of professionalism, and the unnecessary duplication of having supervisors introduce their students individually to the basics of research. Solving these problems requires specifically targeted education to get students started in their graduate research and introduce them to the skills and tools needed to complete it efficiently and effectively. We have used two different approaches in our respective departments. The aim of the programme was to research and develop practical man-machine communication systems. It has been argued since, that the main contribution of this project was not in the development of speech science, but in the development of artificial intelligence.