Supervisor: Professor Josh Reiss
Research group(s): Centre for Digital Music
Sound synthesis is the generation of sounds using algorithms. It is an important application for cinema, multimedia, games and sound installations. This research topic is focused on a ‘big data’ approach to sound synthesis. It addresses the as yet unsolved problem of how to synthesize an entire sound effect library, without creating many different unique models. Learning systems will be fed entire sound effects libraries, capturing the statistics and audio features most associated with classes of sounds. This will be used to adapt the parameter settings of general purpose synthesis models such that they can be tailored to generate sounds that capture the most relevant features of samples or classes of samples in a sound effects library. There are three main challenges in this direction of research; the choice of synthesis model(s) to use, the features used to establish sound similarity, and the learning or optimization technique employed to find the best setting of parameters to ensure that the model generates a close perceptual match to a sound sample. This topic represents a novel research direction with potentially high impact. The researcher will be encouraged to present the research at high impact conferences and publish the results in premier, peer reviewed journals.