Dr Evers is an EPSRC Research Fellow at Imperial College London. She received her PhD from the University of Edinburgh, UK, in 2010, after having completed her MSc degree in Signal Processing and Communications at the University of Edinburgh in 2006, and BSc degree in Electrical Engineering and Computer Science at Jacobs University Bremen, Germany in 2005. After a position as a research associate at the University of Edinburgh between 2009 and 2010, she worked until 2014 as a senior systems engineer on RADAR tracking systems at Selex ES, Edinburgh, UK. She returned to academia in 2014 as a research associate in the Department of Electrical and Electronic Engineering at Imperial College London, focusing on acoustic scene mapping for robot audition. As of 2017, she is awarded a fellowship by the UK Engineering and Physical Sciences Research Council (EPSRC) to advance her research on acoustic signal processing and scene mapping for socially assistive robots. Her research focuses on Bayesian inference for speech and audio applications in dynamic environments, including acoustic simultaneous localization and mapping, sound source localization and tracking, blind speech dereverberation, and sensor fusion. She is an IEEE Senior Member and a member of the IEEE Signal Processing Society Technical Committee on Audio and Acoustic Signal Processing.
et al., 2019, Tracking multiple audio sources with the von mises distribution and variational EM, Ieee Signal Processing Letters, Vol:26, ISSN:1070-9908, Pages:798-802
et al., 2018, DoA reliability for distributed acoustic tracking, Ieee Signal Processing Letters, Vol:25, ISSN:1070-9908, Pages:1320-1324
Hogg A, Evers C, Naylor P, Multiple hypothesis tracking for overlapping speaker segmentation, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), IEEE
Neo V, Evers C, Naylor P, Speech enhancement using polynomial eigenvalue decomposition, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, IEEE
Hogg A, Naylor P, Evers C, Speaker change detection using fundamental frequency with application to multi-talker segmentation, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE