Clustering of strokes from pen-based music notation: An experimental study

Abstract

A comfortable way of digitizing a new music composition is by using a pen-based recognition system, in which the digital score is created with the sole effort of the composition itself. In this kind of systems, the input consist of a set of pen strokes. However, it is hitherto unclear the different types of strokes that must be considered for this task. This paper presents an experimental study on automatic labeling of these strokes using the well-known k-medoids algorithm. Since recognition of pen-based music scores is highly related to stroke recognition, it may be profitable to repeat the process when new data is received through user interaction. Therefore, our intention is not to propose some stroke labeling but to show which stroke dissimilarities perform better within the clustering process. Results show that there can be found good methods in the trade-off between cluster complexity and classification accuracy, whereas others offer a very poor performance.