The Computational Study of a Musical Culture through Its Digital Traces

Abstract

From most musical cultures there are digital traces that can be processed and studied computationally, and this has been the focus of computational musicology. This type of research requires clear formalizations and some simplifications, for example, by considering that a musical culture can be conceptualized as a system of interconnected entities. A musician and a performance are examples of entities, and they are linked by various types of relations. A textual description can be a useful trace of a musician and a recording a trace for a performance. The analytical study of these entities and their interactions is accomplished by processing the digital traces and generating mathematical representations and models of them. A more ambitious goal is to analyze the overall system of interconnected entities to model a musical culture as a whole. In this article I undertake an overview of the state-of-the-art related to this research, identifying current challenges, describing computational methodologies being developed, and summarizing musicologically relevant results of such research. In particular, I review a project in which my colleagues and I have developed audio signal processing, machine learning, and semantic web methodologies to study several musical cultures.