Abstract: The class will be situated at the crossroad of two vibrant fields of research: Music cognition and deep learning in artificial neural networks and related approaches. The field of music cognition aims at understanding the human musical mind and brings together approaches from music theory, psychology and computational modeling. This class will offer an introduction into the field, its central questions and their state-of-the-art. In this context, it will also give a snapshot of some of the main methodologies and approaches that are combined to understand the cognitive processes at the foundation of musical perception and cognition.
In computational modeling, deep learning in artificial neural networks has led to a revolution in artificial intelligence from successful applications to fields such as computer vision, language translation, or games like chess and go. In this class, we will look at the potential of modeling music with artificial neural networks. We will explore ways in which artificial neural networks can be combined with music theoretical and cognitive insights as well as ways in which symbolic and subsymbolic modeling could be bridged.
The class will be complemented with musical and computational exercises.
Course topics include:
- Introduction to music theory and music cognition
- Introduction to artificial neural networks
- The fundamental role of implicit knowledge, expectancy and expectancy formation in musical cognition
- Learning and representation of implicit knowledge in artificial neural networks and probabilistic models
- Musical and computational exercises
Prof. Dr. Martin Rohrmeier, Digital and Cognitive Musicology Lab, EPF Lausanne
Prof. Dr. Wulfram Gerstner, Laboratory of Computational Neuroscience, EPF Lausanne
Open to students of all subject areas, but with some musical background (score reading and
playing an instrument) is required.
To be announced
A reader will be provided electronically
Huron, D. B. (2006). Sweet Anticipation: Music and the Psychology of Expectation. Cambridge, MA: MIT press.
Patel, A. D. (2008). Music, Language, and the Brain. New York: Oxford University Press.
Koelsch, S. (2012). Brain and music. West Sussex, UK: John Wiley & Sons.
Goodfellow I., Bengio Y., Courville A, (2016), Deep Learning. Cambridge MA: MIT Press
Informations générales:-> PDF
Arbeitssprachen: Englisch (und Deutsch)
Koordination: Lydia Tchambaz
Administration: Michelle Hug
Diese Sommerakademie wird mit der Unterstützung der Werner Siemens-Stiftung angeboten.