On comparing edit distance and geometric frameworks in content-based retrieval of symbolically encoded polyphonic music

Abstract

This paper deals with content-based music retrieval (CBMR) of symbolically encoded polyphonic music. It is one of the key issues in the field of music information retrieval. Due to extensive research, there are already satisfactory methods for monophonic CBMR. Unfortunately, this is not the case with the polyphonic task. The problem has been approached in various ways; the majority of the methods suggested fall into two frameworks. The first framework models music as linear strings and the similarity is based on the well-known edit-distance concept. The second one models music as sets of two-dimensional geometric objects (consider the piano-roll representation), but the definition of similarity varies considerably within the framework. We scrutinise these frameworks trying to find common, relevant properties that either inhibit or boost the effectiveness of the methods. Although the edit-distance framework offers more efficient solutions, we conclude that the geometric framework is the choice for the CBMR task because of the very natural way of modelling music still preserving the features intrinsic to the task.