Wilson, AD 
ORCID: https://orcid.org/0000-0002-0013-3650 and Fazenda, BM ORCID: https://orcid.org/0000-0002-3912-0582
2016,
Learning music production practice through evolutionary algorithms
, in: MusTWork16 – ‘Music Technology Workshop 2016: Establishing a Partnership Between Music Technology, Business Analytics and Industry in Ireland', 10th June 2016, Dublin, Ireland.
Abstract
The field of intelligent music production has been an active research topic for over a decade. The
aim is to develop systems which are capable of performing common tasks in music production, such
as level-balancing, equalisation, panning, dynamic range compression and application of artificial
reverberation.
Many systems developed are modelled as expert systems, where the music production task is solved
by optimisation, and domain knowledge, obtained by examining industry “best-practice” methods,
is used to determine the optimisation target [1]. Drawbacks to this method include the fallibility of
domain knowledge and the assumption that there is a global optimum – a mix which all users would
agree is best. Results suggest that many systems can perform basic technical tasks but struggle to
compete with human-made mixes, due to a lack of creativity.
We propose to use interactive evolutionary computation to solve this problem. These methods are
well suited to aesthetic design problems, which are highly non-linear and non-deterministic. In the
case of music mixing, the problem is highly subjective: research has shown that mix engineers
typically prefer their own mix to those of their peers [2]. Consequently, intelligent music production
tools would benefit from interactivity, to determine “personal” global optima in the solution space,
instead of one “universal” global optimum.
The space to be explored is a novel “mix-space” [3]. This space represents all the mixes that it is
possible to create with a finite set of tools. Currently, basic level adjustment has been implemented,
while mix-space representations of panning and equalisation are currently under development.
The fitness function for optimisation is subjective, allowing mixes to be generated in accordance
with any perceptual description, such as “warmth”, “punchiness” or “clarity”. Clustering techniques
are used to increase the population size beyond that which a user could realistically rate, by
extrapolating the fitness function to nearby individuals. When optimising the overall “quality” of
the mix, we introduce findings from recent, large- scale studies of music mixes [4], which can be
used to calculate the fitness of the population, alongside the subjective rating.
Early results indicate that the system can produce a variety of mixes, suited to varying personal
taste. We believe this approach can be used to further the study of intelligent music production, to
deliver personalised object-oriented audio and increase the understanding how music is mixed.
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