Techniques for automated and interactive note sequence morphing of mainstream electronic music

Abstract

Note sequence morphing is the combination of two note sequences to create a {`}hybrid transition{'}, or {`}morph{'}. The morph is a {`}hybrid{'} in the sense that it exhibits properties of both sequences. The morph is also a {`}transition{'}, in that it can segue between them. An automated and interactive approach allows manipulation in realtime by users who may control the relative influence of source or target and the transition length. The techniques that were developed through this research were designed particularly for popular genres of predominantly instrumental electronic music which I will refer to collectively as Mainstream Electronic Music (MEM). The research has potential for application within contexts such as computer games, multimedia, live electronic music, interactive installations and accessible music or {``}music therapy{''}. Musical themes in computer games and multimedia can morph adaptively in response to parameters in realtime. Morphing can be used by electronic music producers as an alternative to mixing in live performance. Interactive installations and accessible music devices can utilise morphing algorithms to enable expressive control over the music through simple interface components. I have developed a software application called LEMorpheus which consists of software infrastructure for morphing and three alternative note sequence morphing algorithms: parametric morphing, probabilistic morphing and evolutionary morphing. Parametric morphing involves converting the source and target into continuous envelopes, interpolation, and converting the interpolated envelopes back into note sequences. Probabilistic morphing involves converting the source and target into probability matrices and seeding them on recent output to generate the next note. Evolutionary morphing involves iteratively mutating the source into multiple possible candidates and selecting those which are judged as more similar to the target, until the target is reached. I formally evaluated the probabilistic morphing algorithm by extracting qualitative feedback from participants in a live electronic music situation, benchmarked against a live, professional DJ. The probabilistic algorithm was competitive, being favoured particularly for long morphs. The evolutionary morphing algorithm was formally evaluated using an online questionnaire, benchmarked against a human composer/producer. For particular samples, the morphing algorithm was competitive and occasionally seen as innovative; however, the morphs created by the human composer typically received more positive feedback, due to coherent, large scale structural changes, as opposed to the forced continuity of the morphing software.