Empirical approaches to Performance: Empirical Musicology II conference

Empirical approaches to Performance: Empirical Musicology II conference (25-26 March 2010, School of Music, University of Leeds, UK)

empirical: "based on, concerned with, or verifiable by observation or experience rather than theory or pure logic : they provided considerable empirical evidence to support their argument." 

musicology: "the study of music as an academic subject, as distinct from training in performance or composition; scholarly research into music."

(Definitions taken from New Oxford American Dictionary 2nd edition © 2005 by Oxford University Press, Inc.)

Whilst musicology can traditionally be highly theoretical, this interdisciplinary conference emphasised an empirical approach, presenting a diverse range of different scientific/practical approaches to the study of music. Focussing on music performance, the conference brought together people from a variety of academic backgrounds to share knowledge and methodologies across disciplines.

The chosen two keynote speakers (Eric Clarke and David Temperley) represented two areas of the spectrum of research covered during the conference. From his standpoint as co-editor of Empirical Musicology: Aims, Methods, Prospects (2004, Oxford University Press) and as an influential musicologist in this field over many years, Eric Clarke gave a historical and critical overview of the use of empirical methods in music research, leading to a project he is currently involved in, the AHRC Research Centre for Musical Performance as Creative Practice (CMPCP). David Temperley brought to his keynote his expertise on probabilistic methods of music analysis and music cognition, discussing how musicians control the flow of musical information during performance.

As an educated guess I believe I was one of very few participants who was not based in a music department (unsurprisingly for a musicology conference!) although in fact, several presenters came from multi-disciplinary research groups. The level of interdisciplinarity demonstrated in the talks did ensure that I didn't feel at all out of place academically, from my standpoint as a music informatician. Many methodologies and tools were being applied outside of their traditional domains to explore a wide range of musical detail, taking advantage of what new technologies have to offer the music researcher. Those that stood out particularly, in my memory at least, were:

• Elaine King and collaborators used a statistical ordination technique borrowed from ecology and educational research (canonical ordination, through the software CANOCO) to cluster together data from participants to extract what students considered their main motivations to prepare for assessed performances. (useful for me as I am looking at how best to cluster large sets of data to extract key themes from the data)
• There was a (beautifully presented) talk from Tal-Chen Rabinowitch on work examining associations between musical interaction in groups of children and their emotional empathic development. To measure the children's emotional empathy, three different measures were used as a battery, of which two came from previous literature and the third was devised for this study. (useful for me as I am looking at how best to measure how creative something is)
• Mark Doffman's presentation on jazz musicians' non-verbal communication focused specifically on how different groups of musicians negotiate how to end an improvisation. His research analysed video footage to examine the communicative behaviour of different types of jazz musicians, using this analysis to examine the musical co-ordination that was happening - (I really identified with this, having more than once been in the position of jamming with other musicians, playing a piece, and having no idea how we were going to make the piece end!)

Organised by Luke Windsor, Karen Burland and Elaine King (on behalf of SEMPRE, a society promoting reserach into music psychology and music education), this conference attracted international participants as well as a large proportion of UK-based researchers. The presentations were on the whole of a good academic standard, with excellent keynote presentations and such a variety of research presented that it was easy to find an angle of interest in most talks. It was a shame that the review process was relatively light, with no critical feedback on abstracts sent back to authors, and perhaps an abstract or two appearing in the proceedings that could have done with a little more editing than they received (although as I myself revised my abstract and title a week before the conference, I am in no position to criticise here..!) This is however only a small negative reflection on what was otherwise an excellently-organised, friendly, academically useful and thought-provoking conference.

To conclude - here is my presentation at this conference, looking at how we can empirically capture what it means to be creative as a musical improviser: 

Defining Creativity in Music Improvisation (presentation slides)

How is creativity manifested in improvisation? We have an intuitive understanding of the concept of creativity that we can use introspectively to suggest answers to these questions, both in theory and during performance. If, though, we want to program a computer to generate music in a creative way, the computer does not understand what creativity is. We cannot ask the computer to behave creatively unless we also give some definition of what such behaviour entails. So the problem becomes: how to define what musical creativity is to a computer.

This work uses empirical methods borrowed from linguistics to capture the words which we strongly associate with creativity. An analysis of the language used in dictionary definitions and academic papers on creativity, as compared to everyday language use, has produced a list of words which we commonly use to discuss creativity, e.g. innovation, openness, divergent. After conducting a survey on how these words can be applied in the context of music improvisation, I empirically derive key attributes of creativity in this musical domain which can be used to guide an artificially intelligent musical system towards generating creative musical behaviour.