Workshop
Music, Brain and Cognition. Part 1: Learning the Structure of Music and Its Effects On the Brain
David R Hardoon · Eduardo Reck-Miranda · John Shawe-Taylor

Fri Dec 7th 07:30 AM -- 06:30 PM @ Westin: Alpine (D-E)
Event URL: http://homepage.mac.com/davidrh/MCBworkshop07/ »

Music is one of the most widespread of human cultural activities, existing in some form in all cultures throughout the world. The definition of music as organised sound is widely accepted today but a naïve interpretation of this definition may suggest the notion that music exists widely in the animal kingdom, from the rasping of crickets' legs to the songs of the nightingale. However, only in the case of humans does music appear to be surplus to any obvious biological purpose, while at the same time being a strongly learned phenomenon and involving significant higher order cognitive processing rather than eliciting simple hardwired responses.

A two day workshop will take place at NIPS 07 (Vancouver, Canada) and will span topics from signal processing and musical structure to the cognition of music and sound. In the first day the workshop will provide a forum for cutting edge research addressing the fundamental challenges of modeling the structure of music and analysing its effect on the brain. It will also provide a venue for interaction between the machine learning and the neuroscience/brain imaging communities to discuss the broader questions related to modeling the dynamics of brain activity. During the second day the workshop will focus on the modeling of sound, music perception and cognition. These have provide, with the crucial role of machine learning, a break through in various areas of music technology, in particular: Music Information Retrieval (MIR), expressive music synthesis, interactive music making, and sound design. Understanding of music cognition in its implied top-down processes can help to decide which of the many descriptors in MIR are crucial for the musical experience and which are irrelevant. The target group is of researchers within the fields of (Music) Cognition, Music Technology, Machine Learning, Psychology, Sound Design, Signal Processing and Brain Imaging.

Author Information

David R Hardoon (SAS)
Eduardo Reck-Miranda (University of Plymouth)
John Shawe-Taylor (UCL)

John Shawe-Taylor has contributed to fields ranging from graph theory through cryptography to statistical learning theory and its applications. However, his main contributions have been in the development of the analysis and subsequent algorithmic definition of principled machine learning algorithms founded in statistical learning theory. This work has helped to drive a fundamental rebirth in the field of machine learning with the introduction of kernel methods and support vector machines, driving the mapping of these approaches onto novel domains including work in computer vision, document classification, and applications in biology and medicine focussed on brain scan, immunity and proteome analysis. He has published over 300 papers and two books that have together attracted over 60000 citations. He has also been instrumental in assembling a series of influential European Networks of Excellence. The scientific coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing.

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