CrowdSound is a collaborative project between researchers at the Universities of Stirling, Aberdeen and Edinburgh. It is funded by the Royal Society of Edinburgh, having formed out of the Scottish Crucible 2016.

We are developing a platform DEASP (Distributed Evolutionary Algorithm for Sounds of Places). This uses evolutionary algorithms, normally used for tackling optimisation problems, sitting on the web to allow a group of people to collaboratively explore what they regard as the distinctive “sound” of a given place.

The team

Sandy Brownlee, University of Stirling. I’m interested in value-added optimisation: techniques that yield optimal or near-optimal solutions but also reveal underlying information about the problem to better support decision making. My main focus is in metaheuristics and related issues such as fitness modelling, mining such models, handling constraints and multiple objectives, and decision support. I am also interested in the underlying theory of what makes particular algorithms suited to particular problems. I work in application areas including scheduling and simulation-based optimsation in civil engineering and transport: crowd-sourcing sounds is a new adventure!

Suk-Jun Kim, University of Aberdeen

Stella Chan, University of Edinburgh

Szu-Han Wan, University of Edinburgh

Jamie Lawson, University of Aberdeen

Pete Stollery, University of Aberdeen