Abstract
Melodic harmonisation is a sophisticated creative process that involves deep musical understanding and a specialised knowledge of music relating to melodic structure, harmony, rhythm, texture, and form. In this article a new melodic harmonisation assistant is presented that is adaptive (learns from data), general (can cope with any tonal or non-tonal harmonic idiom) and modular (learns different aspects of harmonic structure such as chord types, chord transitions, cadences, and voice-leading). This melodic harmonisation system can be used not only to mimic given harmonic styles, but to generate novel harmonisations for diverse melodies and create new harmonic spaces, allowing for the imposition of user-defined chord constraints, leading to new unforeseen harmonic realisations. The various components of the proposed model are explained, then, a number of creative harmonisations of different melodies are presented to illustrate the potential of the system.
Highlights
Creative music systems are often criticised as not “really” being creative per se; underlying this criticism is the belief that the actual human programmer is the true source of creativity
The results presented in Kaliakatsos-Papakostas & Cambouropoulos (2014) indicate that constraint Hidden Markov Model (cHMM) produce harmonisations that are potentially completely different to the ones produced by Hidden Markov Models (HMMs), depending on the imposed constraints
Melodic harmonisation with automated means is a task that requires algorithms exhibiting both emergence of creativity and preservation of structure
Summary
Creative music systems are often criticised as not “really” being creative per se; underlying this criticism is the belief that the actual human programmer is the true source of creativity. Machine learning has made such criticisms more difficult to maintain, as a machine may acquire knowledge from data, constructing a new conceptual space without human intervention, creating new unforeseen output (Wiggins et al, 2009). In the current study the conceptual spaces are learned in a bottom-up fashion from data, and are structured in a modular way so as to allow (at a later stage) to combine different modules from different spaces, creating new blended spaces. At this stage, the system is indicated to exhibit exploratory creativity, by composing harmonies that potentially excess the harmonic borders of a corpus
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