Call for PhD applications at the Machine Listening Lab – 2023 entry

The Machine Listening Lab is welcoming PhD applications for September 2023 entry. Applicants from all nationalities can apply across different funding schemes and PhD programmes. Current PhD funding opportunities for September 2023 entry include:

Applicants are encouraged to contact prospective supervisors before submitting their application – please send an email to your selected supervisors with your CV and draft research proposal.

Suggested PhD topics offered by Machine Listening Lab academics include:

Deep learning for low-resource music
(for PhD in Artificial Intelligence and Music)

Supervisor: Dr. Emmanouil Benetos
in collaboration with Bytedance

The field of music information retrieval (MIR) has been growing for more than 20 years, with recent advances in deep learning having revolutionised the way machines can make sense of music data. At the same time, the MIR community is constrained by the data available, and most methods are focused on extracting information from mainstream music styles (mostly pop, rock, and classical music), using predefined sets of commonly used musical instruments, and when relevant assuming high-resource languages for singing voice analysis. Inspired by recent developments in the field of speech technology for low-resource languages, this PhD project will investigate and develop deep learning methods for making sense of music data in low-resource conditions, whether these refer to under-represented music styles, new musical instruments, or low-resource singing corpora. Methods based on few-shot and zero-shot learning will be investigated, along with methods for open-set recognition, meta-learning or semi-supervised learning, applied to various MIR tasks including but not limited to music tagging, music transcription, lyrics recognition, or audio matching or cover song detection.

The successful candidate will investigate, propose and develop novel methods for analysing low-resource music corpora, resulting in models that can rapidly learn or adapt from small or unlabelled datasets of under-represented music styles, musical instruments, or sung languages.