PhD Candidate: Audio AI for Technological and Neuroscientific Applications at the Donders Centre for Cognition

1 maand geleden


Nijmegen, Nederland Radboud University Voltijd

Universal audio embedding models capable of performing a humanlike range of hearing tasks have numerous technological applications (e.g. augmented hearing, assistive hearing technology) as well as neuroscientific applications (e.g. models of neural encoding). This project aims to leverage self-supervised learning approaches to develop universal audio embedding models which are capable of encoding the full complexity of real-world sound scenes, including the spatial dimension. As part of the research group, you will work with experts in machine learning, digital signal processing and computational auditory neuroscience on new algorithms for real-world, spatial audio representation learning.

Research direction

In this multidisciplinary PhD project, you will focus on designing, implementing and evaluating state-of-the-art self-supervised deep neural network models for universal audio representation learning. You will utilise quantitative analysis techniques to unravel the properties of such universal audio embeddings and employ the developed models and embeddings in a neuroscientific application to investigate brain encoding of real-world sound scenes.

Teaching

You will dedicate 10 % of your working time to teaching activities at undergraduate and postgraduate levels.



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