In order to reach the musicological community and explain the work done within CompMusic, Xavier Serra wrote an article that has just been published in Acta Musicologica, the official peer-reviewed journal of the International Musicological Society.
Serra X. "The computational study of a musical culture through its digital traces". Acta Musicologica. 2017;89(1):24-44.
postprint version: http://hdl.handle.net/10230/32302
Abstract: 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.