Evolutionary music composition is a prominent technique for automatic music generation. The immense adaptation potential of evolutionary algorithms has allowed the realisation of systems that automatically produce music through feature and interactive-based composition approaches. Feature-based composition employs qualitatively descriptive music features as fitness landmarks. Interactive composition systems on the other hand, derive fitness directly from human ratings and/or selection. The paper at hand introduces a methodological framework that combines the merits of both evolutionary composition methodologies. To this end, a system is presented that is organised in two levels: the higher level of interaction and the lower level of composition. The higher level incorporates the particle swarm optimisation algorithm, along with a proposed variant and evolves musical features according to user ratings. The lower level realizes feature-based music composition with a genetic algorithm, according to the top level features. The aim of this work is not to validate the efficiency of the currently utilised setup in each level, but to examine the convergence behaviour of such a two-level technique in an objective manner. Therefore, an additional novelty in this work concerns the utilisation of artificial raters that guide the system through the space of musical features, allowing the exploration of its convergence characteristics: does the system converge to optimal melodies, is this convergence fast enough for potential human listeners and is the trajectory to convergence “interesting’ and “creative” enough? The experimental results reveal that the proposed methodological framework represents a fruitful and robust, novel approach to interactive music composition.

Augmented Reality Audio Games (ARAG) enrich the physical world with virtual sounds to express their content and mechanics. Existing ARAG implementations have focused on exploring the surroundings and navigating to virtual sound sources as the main mode of interaction. This paper suggests that gestural activity with a handheld device can realize complex modes of sonic interaction in the augmented environment, resulting in an enhanced immersive game experience. The ARAG “Audio Legends” was designed and tested to evaluate the usability and immersion of a system featuring an exploration phase based on auditory navigation, as well as an action phase, in which players aim at virtual sonic targets and wave the device to hit them or hold the device to block them. The results of the experiment provide evidence that players are easily accustomed to auditory navigation and that gestural sonic interaction is perceived as difficult, yet this does not affect negatively the system’s usability and players’ immersion. Findings also include indications that elements, such as sound design, the synchronization of sound and gesture, the fidelity of audio augmentation, and environmental conditions, also affect significantly the game experience, whereas background factors, such as age, sex, and game or music experience, do not have any critical impact.

Music composition with algorithms inspired by nature has led to the creation of systems that compose music with rich characteristics. Nevertheless, the complexity imposed by unsupervised algorithms may arguably be considered as undesired, especially when considering the composition of rhythms. This work examines the composition of rhythms through L and Finite L-systems (FL-systems) and presents an interpretation from grammatical to rhythmic entities that expresses the repetitiveness and diversity of the output of these systems. Furthermore, we utilize a supervised training scheme that uses Genetic Algorithms (GA) to evolve the rules of L and FL-systems, so that they may compose rhythms with certain characteristics. Simple rhythmic indicators are introduced that describe the density, pauses, self similarity, symmetry and syncopation of rhythms. With fitness evaluations based on these indicators we assess the performance of L and FL-systems and present results that indicate the superiority of the FL-system in terms of adaptability to certain rhythmic tasks.