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Abstract:
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From most musical cultures there are digital traces, digital artifacts, that can be processed and studied
computationally, and this has been the focus of computational musicology already for several decades.
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, an
instrument, a performance, or a melodic motive, are examples of entities and they are linked by various
types of relationships. We then need adequate digital traces of the entities, for example, a textual
description can be a useful trace of a musician and a recording one of a performance. The analytical study
of these entities and of their interactions is accomplished by processing the digital traces and by
generating mathematical representations and models of them. But a more ambitious goal, however, is to
go beyond the study of individual artifacts and analyze the overall system of interconnected entities in
order to model a musical culture as a whole. The reader might think that this is science fiction, and he or
she might be right, but there is research trying to make advances in this direction. In this article I
undertake an overview the state-of-the-art related to this type of research, identifying current challenges,
describing computational methodologies being developed, and summarizing musicologically relevant
results of such research. In particular, I review the work done within CompMusic, a project in which my
colleagues and I have developed audio signal processing, machine learning, and semantic web
methodologies to study several musical cultures. |
