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effects over the master channel, alongside control of micro-level functions, such as
control over individual pitches or synthesis parameters (Fig. 10.7 ). This approach
provided a framework for addressing performance considerations often associated
with more mainstream digital interfaces. The piece was engineered to communicate
expressive musical control and to provide a loose framework of musical elements
for the performer to navigate through selecting areas for precise manipulation. An
important feature of the design was to emulate the unpredictable nature of per-
forming with acoustic instruments, so often safeguarded in performances with
electronic instruments. Slight deviations of learnt control patterns or miscalcula-
tions when navigating through the piece could result in the wrong result, such as
bringing the composition to an abrupt end or injecting unwanted silences or dis-
sonance into the piece. This approach forced the concentration of the performer,
underlying the importance of successfully interpreting the meaning within the
control EEG. To achieve the desired complexities and nuances, mapping rules were
designed to suit the musical functions, a break away from previous systems where
compositional mappings were intrinsic to the meaning of EEG. Here, the meaning
of EEG was designed through the use of the stimuli and therefore learnt or
understood by the user. With such an abundant amount of meaningful data, The
Warren also makes musical use of non-meaningful data to provide deeper com-
plexity through secondary mappings. For example, ordering rules were applied to
control-speci
s control
behaviour. The order in which icons were selected over x amount of control
changes would result in different generative rules being applied, the results unbe-
known to the performer who would be concentrating on the current primary task.
This harks back to Miranda
c musical parameters through monitoring the performer
'
s technique and the integration of Hjorth analysis,
adding intelligent feedback to the system as part of the compositional process and
making the system learning between performer and computer mutually exclusive.
When designing mappings for a structured performance piece, as opposed to an
instrument or an improvised piece, the mappings need to adapt to the arrangement
of the composition and the functions. This reverse engineering method of mapping
design based on musical function and necessity provides an interesting arena for
creativity. As a result, the mappings explored in The Warren vary widely depending
on the compositional choices, the sonic intentions of composer (and performer) and
the limitations of the input controls. Instead of summarising these mappings solely
in numerical terms, the nature of how the control is governed can be presented in
parallel with Dean and Wellman
'
s( 2010 ) proportional-integral-derivative (PID)
model. This approach defines control as the ' effect ' of the input signal onto the
output
'
s value, regardless of the number of parameters connected. Proportional
control dictates that output values are relative to input; the output is value X
because the input is X. Integral control provides an output value based solely upon
the history of the input, whereas derivative control gives an output value relative to
the rate of change of the input signal.
'
 
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