I am a PhD student in Artificial Intelligence and Music at Queen Mary University of London’s Centre for Digital Music. My research interests include audio synthesis, differentiable signal processing, meta-learning, and musical timbre. I am jointly supervised by Dr Charalampos Saitis and Dr György Fazekas, and am very grateful to be funded by the UKRI Centre for Doctoral Training in Artificial Intelligence and Music.
Previously, I was Music Lead at the award-winning AI-driven generative music startup Jukedeck, and was a research intern with ByteDance’s Speech, Audio & Music Intelligence (SAMI) team. I also make music and teach undergraduate Electronic and Produced music at the Guildhall School of Music and Drama.
I am particularly open to collaborations with musicians and artists looking to apply artificial intelligence to their work, and engineers interested in building new musical tools, and researchers working on related topics.
You can contact me at b.j.hayes (at) qmul.ac.uk
- arXivSinusoidal Frequency Estimation by Gradient DescentIn arXiv 2022
- ICAtimbre.fun: A gamified interactive system for crowdsourcing a timbre semantic vocabularyIn 24th International Congress on Acoustics 2022
- JAESDisembodied Timbres: A Study on Semantically Prompted FM SynthesisIn Journal of the Audio Engineering Society 2022
- ISMIRNeural Waveshaping SynthesisIn Proceedings of the 22nd International Society for Music Information Retrieval Conference 2021