TraSe algorithm : automatic evolutionary morphing of electronic dance music

The TraSe (Transform-Select) algorithm has been developed to investigate the morphing of electronic music through automatically applying a series of deterministic compositional transformations to the source, guided towards a target by similarity metrics. This is in contrast to other morphing techniques such as interpolation or parameters or probabilistic variation. TraSe allows control over stylistic elements of the music through user-defined weighting of numerous compositional transformations. The formal evaluation of TraSe was mostly qualitative and occurred through nine participants completing an online questionnaire. The music generated by TraSe was generally felt to be less coherent than a human composed benchmark but in some cases judged as more creative.

Note sequence morphing algorithms for performance of electronic dance music

This paper describes algorithms that can musically augment the realtime performance of electronic dance music by generating new musical material by morphing. Note sequence morphing involves the algorithmic generation of music that smoothly transitions between two existing musical segments. The potential of musical morphing in electronic dance music is outlined and previous research is summarised; including discussions of relevant music theoretic and algorithmic concepts. An outline and explanation is provided of a novel Markov morphing process that uses similarity measures to construct transition matrices. The paper reports on a ‘focus-concert’ study used to evaluate this morphing algorithm and to compare its output with performances from a professional DJ. Discussions of this trial include reflections on some of the aesthetic characteristics of note sequence morphing. The research suggests that the proposed morphing technique could be effectively used in some electronic dance music contexts.

Music purchase behaviour : the effect of emotional loyalty on intention to purchase.

This paper explores the influence of emotional loyalty on music purchase behaviour. Specifically, it examines whether emotional attachment to an artist's music influences loyalty to that artist, and how emotional loyalty influences a consumer's decision to purchase music. Data collection involved fifteen semi-structured interviews with young (18-30) subjects recruited through non-probability convenience sampling. Findings show that consumers who are emotionally loyal to an artistes) are more inclined to purchase the music rather than download free of charge.

Technical support and professional development : using remote desktops to address bottlenecks in music technology

This paper presents research findings about the use of remote desktop applications to teach music sequencing software. It highlights the successes, shortcomings and interactive issues encountered during a pilot project with a theoretical focus on a specific interactive bottleneck. The paper proposes a new delivery and partnership model to widen this bottleneck, which currently hinders interactions between the technical support, education and professional development communities in music technology.