About
Christine Evers is a Lecturer (Assistant Prof. equivalent) in Computer Science. Her research focuses on Bayesian learning for machine listening, with a particular focus on robot audition. Her research is located on the intersection of robotics, machine learning, acoustics, and statistical signal processing. She is a Co-I on the Trustworthy Autonomous Systems Hub, and the cohort lead as well as the theme lead for 'Embedded AI' on the UKRI Centre for Doctoral Training in Machine Intelligence for Nano- Electronic Devices and Systems (MINDS).
Prior to joining the University of Southampton, Christine was the recipient of an EPSRC Fellowship to advance her work on "Acoustic Signal Processing and Scene Analysis for Socially Assistive Robots", hosted at Imperial College London. Her fellowship followed a position a research associate on the FP7 project "Embodied Audition for Robots" at Imperial College. Christine has previously worked in the industry as a senior systems engineer at Selex ES, Edinburgh (UK). She received her PhD in statistical signal processing under the supervision of Dr James R. Hopgood from the University of Edinburgh, UK.
Christine is a Senior Member of the IEEE, an elected member of the IEEE Signal Processing Society (SPS) Technical Committee on Audio and Acoustic Signal Processing, a member of the IEEE SPS Challenges and Data Collections committee, and serves as an associate editor for IEEE Transactions on Audio, Speech & Language Processing as well as the EURASIP Journal on Audio, Speech, and Music Processing. She has served on several conference organising committees and is a regular reviewer for various journals and conferences across robotics, acoustics, and machine learning.
Full publication list on Google Scholar
Research
Research groups
Current research
My research is in the area of machine listening, and equips autonomous agents with the ability to extract semantic cues from microphone signals in order to answer the questions "Where am I?", "What are the sounds around me?", and "How can I interact in my acoustic environment?". The objective of my research is to provide agents, such as robots, with situational awareness in order to reason and interact within complex, acoustic environments, affected by interference, noise, and reverberation.
Research projects
Active projects
Publications
Supervision
Current PhD Students
Teaching
External roles and responsibilities
Biography
Christine Evers is a Lecturer (Assistant Prof. equivalent) in Computer Science. Her research focuses on Bayesian learning for machine listening, with a particular focus on robot audition. Her research is located on the intersection of robotics, machine learning, acoustics, and statistical signal processing. She is a Co-I on the Trustworthy Autonomous Systems Hub, and the cohort lead as well as the theme lead for 'Embedded AI' on the UKRI Centre for Doctoral Training in Machine Intelligence for Nano- Electronic Devices and Systems (MINDS).
Prior to joining the University of Southampton, she was the recipient of an EPSRC Fellowship to advance her work on "Acoustic Signal Processing and Scene Analysis for Socially Assistive Robots", hosted at Imperial College London. Her fellowship followed a position a research associate on the FP7 project "Embodied Audition for Robots" at Imperial College. She has previously worked in the industry as a senior systems engineer at Selex ES, Edinburgh (UK). She received her PhD in statistical signal processing under the supervision of Dr James R. Hopgood from the University of Edinburgh, UK.
She is a Senior Member of the IEEE, a member of the IEEE SPS Challenges and Data Collections committee, and serves as an associate editor for IEEE Transactions on Audio, Speech & Language Processing as well as the EURASIP Journal on Audio, Speech, and Music Processing. She has served two terms as an elected member of the IEEE Signal Processing Society (SPS) Technical Committee on Audio and Acoustic Signal Processing, served on several conference organising committees, and is a regular reviewer for various journals and conferences across robotics, acoustics, and machine learning.
Full publication list on Google Scholar