Timea Farkas

Timea Farkas is a composer/sound artist with an interest in implementing music and sound into other disciplines with unconventional methods. She has worked with sound in a variety of ways, including virtual reality environments, theatre, and - with the use of creative coding - turning simple games into controllers for music performance. Her research interests include analogue games, generative music systems and machine learning. Timea has co-organised Composer/Computer/Distance, a two day electronic music conference that took place in May, 2018, which provided a platform that celebrates diversity in the electronic music field, making computer music accessible to the general public and bringing together experts and emerging artists from marginalised groups. She holds an MA in Sonic Arts from the University of Sheffield and has graduated with a First Class (Hons) degree in music composition and technology with a special award for outstanding achievement and collaboration.

Developing Hidden Interaction Solutions to Enable the Use of Adaptive Soundtracks in Accompanying Modern, Narrative Driven Board Games

The popularity of board games has had a steep elevation in recent years partly because of their increasing availability in digital form. One feature of the digital adaptations is that they all include adaptive music and sound design, which is an immersion-enhancing feature, currently absent from the experience of playing analogue tabletop games. While the inclusion of original soundtracks is on the rise in the board games industry, in their current form, these soundtracks can only accommodate games with low consequence randomness – a type of randomness that causes unpredictable changes to the game state and gameplay experience. As tabletop games are becoming increasingly narrative-driven – a feature that results in higher immersion levels and higher consequence randomness – a dynamic, adaptive soundtrack system is needed that is able to follow game state changes accordingly, resulting in gaming experiences on par with digital games. The goal of this research is to create such a system, exploring the concept of hidden interaction (interaction without the user’s conscious interference) with the tools of machine learning and adaptive music while attempting to gain an understanding of immersion in analogue games from an experiential perspective.