You are all cordially invited to the next AMLab colloquium on Tuesday, December 15 at 16:00 in C3.163, where John Ashley Burgoyne from the music cognition group here at the UvA will give a talk titled “‘Big data’, music-cognition style”.
Abstract: Machine learning techniques have been fundamental in the development of music information retrieval (MIR), but there has been much more resistance to applications in music cognition (Aucouturier & Bigand, 2013). As more cognition experiments collect relatively large amounts data, however – Internet-based experiments, for example, or recordings of live environments – interest in machine learning is growing.
This talk will introduce two ongoing projects at the University of Amsterdam’s Music Cognition Group that could benefit from machine-learning approaches. The first, Hooked on Music, is an online music game, now played more than 3 million times, that seeks to identify which musical characteristics may be responsible for long-term musical memories. The modelling challenge is balancing predictive accuracy with interpretability from a musical perspective. The second is a series of overhead video recordings of silent discos, popular dance events during which music is streamed to participants on multi-channel headphones. Here the modelling challenge is in the pre-processing: we would like to test various hypotheses about music and social behaviour, but this is only possible if we can get reasonably accurate location tracking of the LED lights on each set of silent-disco headphones.
In both cases, I will discuss our modelling approaches and results to date, where those models might be improved using machine learning approaches, and how it might be most interesting to collaborate.