=Paper=
{{Paper
|id=Vol-1520/paper31
|storemode=property
|title=Virtuosity in Computational Performance
|pdfUrl=https://ceur-ws.org/Vol-1520/paper31.pdf
|volume=Vol-1520
|dblpUrl=https://dblp.org/rec/conf/iccbr/Goddard15
}}
==Virtuosity in Computational Performance==
https://ceur-ws.org/Vol-1520/paper31.pdf
268
Doctoral Consortium Research Summary:
Virtuosity in Computational Performance
Callum Goddard
School of Electronic Engineering and Computer Science,
Queen Mary University of London, Mile End Road,
London E1 4NS, United Kingdom
c.goddard@qmul.ac.uk
http://www.eecs.qmul.ac.uk/
Abstract. This is a research summary of Virtuosity in Computational
Performance, addressing the question: How can a computer, as judged
by a human audience, demonstrate virtuosity in computational perfor-
mances with a physical model of a bass guitar? The proposed plan for this
research is to develop a computational performance system which uses
case-based reasoning and reflection to produce virtuosic performances
with a physical model of an electric bass guitar. Three supporting stud-
ies are planned to investigate bass playing, collect performance data and
perceptions of virtuosity.
Keywords: Computational Performance, Virtuosity, Case-based Rea-
soning, Reflection, Physical Modelling, Music Analysis
1 The Problem being Addressed and Research Questions
The main question this research is addressing is:
How can a computer, as judged by a human audience, demonstrate vir-
tuosity in computational performances with a physical model of a bass
guitar?
Computationally performed music, where a computer is responsible for ren-
dering, generating or synthesising the music in its entirety, can appear lacking,
robotic or sterile [1]. There are approaches to overcome this that focus on in-
troducing or emulating expressive phrasing within a performance of a score [1].
However, if instead of expression virtuosity was exhibited within a computer per-
formance, would this not o↵er a more satisfying solution to sterile performances
as well as aiding in investigations into virtuosity of human performances?
Virtuosity here is being viewed as a property of a performance, formed
through a complex and dynamic relationship between the performer, an audience
and the domain in which the performance is situated [2]. It encompasses notions
of expression and style within the performance alongside a demonstration of
high levels of technical proficiency, a deeper understanding of the instrument,
the piece being played and the context or domain of the performance.
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes. In Proceedings of the ICCBR 2015 Workshops. Frankfurt, Germany.
269
2 Research Summary: Virtuosity in Computational Performance
The decision to limit the scope of this research to the domain of electric bass
has been made as the author is an experienced electric bass player. There is also
recent research [3, 4] within this area that can be used within this PhD.
2 Proposed Plan for Research
To address the main research question, this research will focus on developing a
theory for how a computer can exhibit virtuosity within a rendered performance.
To allow this theory to be tested a computer performance system that can cre-
ate performances, using the physical model of electric bass guitar developed by
Kramer et al. [3], is planned to be developed.
The current theory is based upon a case-based reasoning approach. Previous
work on the SaxEx system [5] has demonstrated how e↵ective case-based reason-
ing can be when applied to creating expressive performances. Unlike the SaxEx
system, which manipulates the waveforms of a non-expressive audio recording as
its output, the planned system will be manipulating physical model parameters.
These parameters are intended to be abstracted to allow for rationalisation of
the performances and evaluation of their virtuosity.
A performance here is being formalised in Equation 1 as the result of P layer
applying a set of T echniques, {Tpluck , Tthumb ...Tn }, to a sequence of N otes,
hN1 , N2 ...N ni. Musical score information, physical model and performance pa-
rameters are needed to be represented, abstracted and manipulated to produce
a performance. All this information will be represented using the Common Hi-
erarchical Abstract Representation for Music (CHARM) [7, 8].
P erf ormance = P layer(T echniques, N otes) (1)
Cases are to be CHARM constituents. Constituents are formed by grouping
together particles. Particles can be either events and/or other constituents. An
event di↵ers from a constituent in that it is the most fundamental element of
interest within the data and as such cannot be formed from groupings. Con-
stituents enable the formation of hierarchical structures, denoting increases in
both hierarchical level and in abstraction. Events form the lowest levels of this
hierarchy and within this research will be musical notes. A visual representation
of an example is constituent is shown in Figure 1.
When producing a new performance, or interpreting one, a new CHARM
representation will need to be constructed. First, constituents of suitable types
are found, or created, and then searches for similar constituents are made. A
constituent’s similarity is to be judged on its structural and musical type, along
with the combination and type of its particles. Retrieved constituents can the
be modified by interchanging particles for better matching ones to increase the
suitability of the constituent for the new case. This process of finding new con-
stituents, then modifying them is akin to the engagement reflection cycle out-
lined by Pérez y Pérez [6], and is important as being able to reflect upon the
performances the system creates can help to guide it towards producing virtuosic
one.
270
Research Summary: Virtuosity in Computational Performance 3
Constituent:
Increasing Levels of
(Identifier, Structural Type, Musical Type)
Abstraction
Particles:
Event:
(Identifier, Pitch, Time, Duration, Timbre)
Event: Constituent:
(Identifier, Pitch, Time, Duration, Timbre) (Identifier, Structural Type, Musical Type) Particles of the
constituent
Fig. 1. A visual representation of a CHARM constituent formed of a group of three
particles: two events and a constituent, (which has its own particles). I refer the reader
to Smaill et al. [7] for more details on the internal structure of event and constituents.
Ontologies for domain specific knowledge e.g. musical score structure, bass
technique etc. will be separate from the CHARM representation forming add-on
modules for the system. To further inform the knowledge required by the system
three studies are planned. One to investigate aspects of bass playing, one to
collect performance data and third to see how virtuosity is perceived to inform
the reflection of the system.
3 Description of Progress to date
At present I am approaching the first year review of my PhD. The work so far
has been in better understanding the form the PhD will be taking, with this
document forming a brief summary of the work that has been completed so far.
Acknowledgments. This work is supported by the Media and Arts Technology
programme, EPSRC Doctoral Training Centre EP/G03723X/1
References
1. Widmer, G. and Goebl, W. Computational models of expressive music performance:
The state of the art. JNMR, 33(3):203-216. (2004)
2. Howard, V. A.: Charm and speed: virtuosity in the performing arts. Peter Lang
Publishing Inc., New York. (2008)
3. Kramer, P., Abesser, J., Dittmar, C., and Schuller, G.: A digitalwaveguide model of
the electric bass guitar including di↵erent playing techniques. ICASSP, IEEE (2012)
4. Abeßer, J.: Automatic Transcription of Bass Guitar Tracks applied for Music Genre
Classification and Sound Synthesis. PhD thesis, Elektrotechnik und Information-
stechnik der Technischen Universität Ilmenau (2014)
5. Arcos, J. L., Mántaras, R. L., and Serra, X.: Saxex: a case-based reasoning system
for generating expressive musical performances. JNMR, 27(3):194-210. (1998)
6. Pérez y Pérez, R.,. MEXICA: A Computer Model of Creativity in Writing. PhD
thesis, The University Of Sussex. (1999)
7. Smaill, A., Wiggins, G., and Harris, M.: Hierarchical music representation for com-
position and analysis. CHum. (1993)
8. Wiggins, G., Miranda, E., Smaill, A., and Harris, M.:A framework for the evaluation
of music representation systems. CMJ, 17(3):31-42. (1993)