A Data Mining Approach to Expressive Music Performance Modeling
In this chapter we present a data mining approach to one of the most challenging aspects of computer music: modeling the knowledge applied by a musician when performing a score in order to produce an expressive performance of a piece. We apply data mining techniques to real performance data (i.e., audio recordings) in order to induce an expressive performance model. This leads to an expressive performance system consisting of three components: (1) a melodic transcription component that extracts a set of acoustic features from the audio recordings, (2) a data mining component that induces an expressive transformation model from the set of extracted acoustic features, and (3) a melody synthesis component that generates expressive monophonic output (MIDI or audio) from inexpressive melody descriptions using the induced expressive transformation model. We describe, explore, and compare different data mining techniques for inducing the expressive transformation model.
Dennis M. Ray is a visiting scholar at both the Institute of South-east Asian Studies, Singapore, and the Institute of International Relations, Sophia University, Tokyo, Japan. This research note reports on a pilot study conducted in Singapore to test whether two scales correlated with either the level of exports or the manner in which export sales were pursued by venture business in Singapore. The study suggests that a cross-cultural experience scale along and in combination with the craftsman-opportunist typology modified to fit various culture settings may be a powerful tool for predicting export activity.