Acoustic Speaker Localization with Strong Reverberation and Adaptive Feature Filtering with a Bayes RFS Framework
Access Status
Open access
Authors
Lin, Shoufeng
Date
2019Supervisor
Ling Li
Type
Thesis
Award
PhD
Metadata
Show full item recordFaculty
Science and Engineering
School
Electrical Engineering, Computing and Mathematical Sciences
Collection
Abstract
The thesis investigates the challenges of speaker localization in presence of strong reverberation, multi-speaker tracking, and multi-feature multi-speaker state filtering, using sound recordings from microphones. Novel reverberation-robust speaker localization algorithms are derived from the signal and room acoustics models. A multi-speaker tracking filter and a multi-feature multi-speaker state filter are developed based upon the generalized labeled multi-Bernoulli random finite set framework. Experiments and comparative studies have verified and demonstrated the benefits of the proposed methods.
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