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Machine Learning of Procedural Audio Theme Game Audio Project proposed & supervised by Joshua Reiss To discuss whether this project could become your PhD proposal please email: joshua.reiss@qmul.ac.uk < Back Machine Learning of Procedural Audio Project proposal abstract: Game sound design relies heavily on pre-recorded samples, but this approach is inflexible, repetitive and uncreative. An alternative is procedural audio, where sounds are created in real-time using software algorithms. But many procedural audio techniques are low quality, or tailored only to a narrow class of sounds. Machine learning from sample libraries to select, optimise and improve the procedural models, could be the key to transforming the industry and creating procedural auditory worlds. This work will build on recent high impact research from the team to investigate whether procedural audio can fully replace the use of pre-recorded sound effects. See https://nemisindo.com for examples of procedural sound effects. Supervisor: Joshua Reiss Based at: This project will be a collaboration with Nemesindo .

< Back Emotion Design for Video Games: A Framework for Affective Interactivity Link Author(s) M Croissant, G Schofield, C McCall Abstract More info TBA Link

< Back MEG adaptation resolves the spatiotemporal characteristics of face-sensitive brain responses Link Author(s) MIG Simpson, SR Johnson, G Prendergast, AV Kokkinakis, E Johnson, ... Abstract More info TBA Link

iGGi Partners We are excited to be collaborating with a number of industry partners. IGGI works with industry in some of the following ways: Student Industry Knowledge Transfer - this can take many forms, from what looks like a traditional placement, to a short term consultancy, to an ongoing relationship between the student and their industry partner. Student Sponsorship - for some of our students, their relationship with their industry partner is reinforced by sponsorship from the company. This is an excellent demonstration of the strength of the commitment and the success of the collaborations. In Kind Contributions - IGGI industry partners can contribute by attending and/or featuring in our annual conference, offering their time to give talks and masterclasses for our students, or even taking part in our annual game jam! There are many ways for our industry partners to work with IGGI. If you are interested in becoming involved, please do contact us so we can discuss what might be suitable for you. GameSparks: Amazon

< Back From Code to Play: Benchmarking Program Search for Games Using Large Language Models Link Author(s) M Eberhardinger, J Goodman, A Dockhorn, D Perez-Liebana, RD Gaina, ... Abstract More info TBA Link

< Back Computational models for the analysis and synthesis of graffiti tag strokes Link Author(s) D Berio, FF Leymarie Abstract More info TBA Link

< Back "These People Had Taken Advantage of Me”: A Grounded Theory of Problematic Consequences of Player Interaction with Mobile Games Perceived as “Designed to Drive Spending" Link Author(s) E Petrovskaya, D Zendle Abstract More info TBA Link

< Back Towards human-like artificial intelligence using StarCraft 2 Link Author(s) H Siljebrat, C Addyman, A Pickering Abstract More info TBA Link

< Back The Sky's the Limit: Relightable Outdoor Scenes via a Sky-Pixel Constrained Illumination Prior and Outside-In Visibility Link Author(s) JAD Gardner, E Kashin, B Egger, WAP Smith Abstract More info TBA Link

< Back Analysis of statistical forward planning methods in Pommerman Link Author(s) D Perez-Liebana, RD Gaina, O Drageset, E Ilhan, M Balla, SM Lucas Abstract More info TBA Link

< Back UKRN Local Network Lead Guidebook Link Author(s) UKR Network, SJ Westwood, DL Hird, N Ballou, M Belyk, C Bokhove, ... Abstract More info TBA Link

< Back Gamifying language resource acquisition Link Author(s) CJ Madge Abstract More info TBA Link