Abstract

Abstract: Implicit learning constitutes a root process in the human interaction with the environment. Most processes in music perception and cognition are governed by a large body of implicit knowledge acquired through massive exposure to and interaction with music during enculturation processes independently of musical expertise or background. However, real-world musical structure encompasses a complexity which still eludes comprehensive structural description or computational tractability (in comparison to even the most basic human abilities). Accordingly, one of the key notions to improve computational models and processing and to better understand music perception is to study how humans manage to deal with the complexity and to explore human processes of implicit learning in experimental conditions and computational modelling. Such an understanding is foundational for the development of flexible, practically applicable (cognitively adequate) methods of computational music analysis, automated music (style) classification and music information retrieval.

The talk aims to give an overview about the research in this area that was performed by Ian Cross and myself during the past years; it will present and relate findings from experimental, computational and music information-retrieval perspectives.