Acoustic Speaker Localization with Strong Reverberation and Adaptive Feature Filtering with a Bayes RFS Framework
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...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2019
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| Online Access: | http://hdl.handle.net/20.500.11937/76069 |
| _version_ | 1848763624937488384 |
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| author | Lin, Shoufeng |
| author_facet | Lin, Shoufeng |
| author_sort | Lin, Shoufeng |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:06:26Z |
| format | Thesis |
| id | curtin-20.500.11937-76069 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:06:26Z |
| publishDate | 2019 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-760692021-08-17T02:42:10Z Acoustic Speaker Localization with Strong Reverberation and Adaptive Feature Filtering with a Bayes RFS Framework Lin, Shoufeng 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. 2019 Thesis http://hdl.handle.net/20.500.11937/76069 Curtin University fulltext |
| spellingShingle | Lin, Shoufeng Acoustic Speaker Localization with Strong Reverberation and Adaptive Feature Filtering with a Bayes RFS Framework |
| title | Acoustic Speaker Localization with Strong Reverberation and Adaptive Feature Filtering with a Bayes RFS Framework |
| title_full | Acoustic Speaker Localization with Strong Reverberation and Adaptive Feature Filtering with a Bayes RFS Framework |
| title_fullStr | Acoustic Speaker Localization with Strong Reverberation and Adaptive Feature Filtering with a Bayes RFS Framework |
| title_full_unstemmed | Acoustic Speaker Localization with Strong Reverberation and Adaptive Feature Filtering with a Bayes RFS Framework |
| title_short | Acoustic Speaker Localization with Strong Reverberation and Adaptive Feature Filtering with a Bayes RFS Framework |
| title_sort | acoustic speaker localization with strong reverberation and adaptive feature filtering with a bayes rfs framework |
| url | http://hdl.handle.net/20.500.11937/76069 |