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Bayesian source of interest extraction
Dissertation (MEng (Bioengineering))--University of Pretoria, 2025.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2026
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| _version_ | 1869484088501469184 |
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| access_status_str | Open Access |
| author2 | Hanekom, J.J. (Johannes Jurgens) |
| author_browse | Hanekom, J.J. (Johannes Jurgens) |
| author_facet | Hanekom, J.J. (Johannes Jurgens) |
| collection | Thesis |
| dc_rights_str_mv | © 2024 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng (Bioengineering))--University of Pretoria, 2025. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/107542 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-07-01T04:09:20.134Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/107542 Bayesian source of interest extraction Hanekom, J.J. (Johannes Jurgens) u05026793@tuks.co.za Hanekom, Natalie UCTD Sustainable Development Goals (SDGs) Gaussian mixture model Source of interest extraction Bayesian source separation Variational Bayes Dissertation (MEng (Bioengineering))--University of Pretoria, 2025. This dissertation presents a fully Bayesian multivariate source separation model and algorithm. Full posterior model parameter and latent variable distributions were inferred using variational Bayes. The algorithm can be seen as variational Bayesian independent vector analysis for simultaneously separating multiple mixtures, such as those obtained by converting a convolutive mixture into a time-frequency domain. Key properties of the model are Gaussian mixture model source models and an explicit noise model. The algorithm was developed for guided audio source separation in a time-frequency domain. The explicit use of prior information makes Bayesian inference a natural choice in modern guided source separation algorithms, which use available information to guide algorithms to specific solutions and to achieve maximum performance for a given problem. Sources of interest were extracted from mixtures by utilising speaker-dependent Gaussian mixture model priors. The speaker-dependent models were learned from short enrolment utterances of the sources of interest, which are easily obtainable using, e.g., a mobile phone. Interfering speakers and other noise sources were jointly modelled by the noise component in the model.The algorithm was evaluated in realistic conditions with conversational speech, reverberation and diffuse babble noise from several background speakers. Speaker identification was complicated by speakers of the same gender, including related speakers (mother and daughter). The algorithm was compared to state-of-the-art source of interest extraction algorithms and achieved the best performance, frequently obtaining 10 dB better interference and noise suppression than the second-best-performing algorithm. Electrical, Electronic and Computer Engineering MEng (Bioengineering) Unrestricted Faculty of Engineering, Built Environment and Information Technology SDG-03: Good health and well-being SDG-09: Industry, innovation and infrastructure SDG-10: Reduces inequalities 2026-01-23T08:49:12Z 2026-01-23T08:49:12Z 2026-04 2025 Dissertation * A2026 http://hdl.handle.net/2263/107542 en © 2024 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Sustainable Development Goals (SDGs) Gaussian mixture model Source of interest extraction Bayesian source separation Variational Bayes Bayesian source of interest extraction |
| title | Bayesian source of interest extraction |
| title_full | Bayesian source of interest extraction |
| title_fullStr | Bayesian source of interest extraction |
| title_full_unstemmed | Bayesian source of interest extraction |
| title_short | Bayesian source of interest extraction |
| title_sort | bayesian source of interest extraction |
| topic | UCTD Sustainable Development Goals (SDGs) Gaussian mixture model Source of interest extraction Bayesian source separation Variational Bayes |
| url | http://hdl.handle.net/2263/107542 |