DERI Seminar with Dr Emmanouil Benetos who is Reader in Machine Listening and Director of Research at the School of Electronic Engineering and Computer Science at QM
When: Thursday, March 20, 2025 - Thursday, February 20, 2025, 11:00 AM - 12:00 PM
Where: Teams
Speaker: Fellow Dr Emmanouil Benetos, ECCS, QM
Title: Machine learning paradigms for music and audio understanding
Abstract:
The area of computational audio analysis -also called machine listening- continues to evolve. Starting from methods grounded in digital signal processing and acoustics, followed by supervised machine learning methods that require large amounts of labelled data, recent approaches for learning audio representations are fuelled by advances in the broader field of artificial intelligence. The talk will outline recent research carried out at the Centre for Digital Music of QMUL focusing on emerging learning paradigms for making sense of music and audio data. Topics covered will include learning in the presence of limited audio data, the inclusion of other modalities such as natural language to aid learning audio representations, and finally methods for learning from unlabelled audio data - with the latter being used as a first step towards the creation of music foundation models.
Bio: Emmanouil Benetos is Reader in Machine Listening and Director of Research at the School of Electronic Engineering and Computer Science of Queen Mary University of London. Within Queen Mary, he is member of the Centre for Digital Music and Centre for Multimodal AI, is academic fellow at the Digital Environment Research Institute, is Deputy Director for the UKRI Centre for Doctoral Training in AI and Music (AIM), and co-leads the School's Machine Listening Lab. His main area of research is computational audio analysis, also referred to as machine listening or computer audition - with applications to music, urban, everyday and nature sounds. Website: https://www.eecs.qmul.ac.uk/~emmanouilb/
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Microsoft Teams DERI Seminar with Emmanouil Benetos
Meeting ID: 362 910 387 247
Passcode: L9X5hi6R
For organizers: Meeting options