Understanding the Virtuoso: Exploring Music and Performance with Software

Nicolas Gold (University College London)

February 10th, 14:00 in KE LT1

There are many applications of computing in support of music analysis.  In
this talk I will discuss two recent pieces of research in this area.  The
first aims to investigate why we prefer listening to some musicians over
others playing the same pieces (a search for "performance motive").  I
will discuss the methodology we developed that uses automated clustering
techniques to analyse a larger corpus of recorded performances than would
be possible for an analyst working alone.  The method characterises the
performed shape of structural aspects of the music and allows comparison
between performers.  The second piece of research addresses the problem of
clone detection in patches written in the Max/MSP programming language,
widely used for interactive music and art.  The problems of clone
detection are somewhat different for graphical data-flow languages like
Max/MSP in comparison to traditional languages, and require a new approach
to source code analysis.

Nicolas Gold is a Senior Lecturer in the Department of Computer Science at
University College London (UCL) and received his Ph.D. from the University
of Durham, UK, in 2000.  He undertakes research in music computing,
software engineering and the digital humanities and has published widely
in these areas.  He is a member of the Centre for Research in Evolution,
Search, and Testing (CREST), an affiliate member of the UCL Centre for
Digital Humanities, and an Associate of the AHRC Research Centre for
Musical Performance as Creative Practice (CMPCP).