Christine Evers
Papers
10
Total Citations
301
H-Index
7
About
Christine Evers is a leading researcher in robot audition and acoustic signal processing, whose work bridges the gap between autonomous systems and human-robot interaction. Her primary research areas include acoustic simultaneous localization and mapping (SLAM), source tracking, and speech enhancement. Evers is best known for pioneering "Acoustic SLAM," an algorithm that enables robots equipped with microphone arrays to explore environments and create three-dimensional maps of sound sources—a breakthrough that has garnered over 100 citations. She also contributed to the LOCATA Challenge, a benchmark dataset for acoustic source localization and tracking that has become a standard evaluation tool in the field, also cited over 100 times. Her work on bearing-only acoustic tracking and moving microphone arrays has advanced robot audition in real-world, reverberant environments, addressing challenges like missing detections and interference. Evers has also developed polynomial eigenvalue decomposition (PEVD) methods for speech dereverberation and source separation, improving intelligibility in noisy settings. With a strong focus on practical applications—from hearing aids to teleconferencing—her research has shaped how autonomous systems perceive and interact with their acoustic surroundings.
Research Focus
Key Achievements
Top Papers
- 1Acoustic SLAM103 citations · 2018
- 2The LOCATA Challenge Data Corpus for Acoustic Source Localization and Tracking103 citations · 2018
- 3Bearing-only acoustic tracking of moving speakers for robot audition28 citations · 2015
- 4Source tracking using moving microphone arrays for robot audition26 citations · 2017
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- 6PEVD-Based Speech Enhancement in Reverberant Environments10 citations · 2020
- 7Speech Dereverberation Performance of a Polynomial-EVD Subspace Approach7 citations · 2020
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- 10Towards Informative Path Planning for Acoustic SLAM2 citations · 2016