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Investigating audio classification to automate the trimming of recorded lectures
With the demand for recorded lectures to be made available as soon as possible, the University of Cape Town (UCT) needs to find innovative ways of removing bottlenecks in lecture capture workflow and thereby improving turn-around times from capture to publication. UCT utilises Opencast, which is an...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2019
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| _version_ | 1867613237252456448 |
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| access_status_str | Open Access |
| author | Govender, Devandran |
| author2 | Suleman, Hussein |
| author_browse | Govender, Devandran Suleman, Hussein |
| author_facet | Suleman, Hussein Govender, Devandran |
| author_sort | Govender, Devandran |
| collection | Thesis |
| description | With the demand for recorded lectures to be made available as soon as possible, the University of Cape Town (UCT) needs to find innovative ways of removing bottlenecks in lecture capture workflow and thereby improving turn-around times from capture to publication. UCT utilises Opencast, which is an open source system to manage all the steps in the lecture-capture process. One of the steps involves manual trimming of unwanted segments from the beginning and end of video before it is published. These segments generally contain student chatter. The trimming step of the lecture-capture process has been identified as a bottleneck due to its dependence on staff availability. In this study, we investigate the potential of audio classification to automate this step. A classification model was trained to detect 2 classes: speech and non-speech. Speech represents a single dominant voice, for example, the lecturer, and non-speech represents student chatter, silence and other environmental sounds. In conjunction with the classification model, the first and last instances of the speech class together with their timestamps are detected. These timestamps are used to predict the start and end trim points for the recorded lecture. The classification model achieved a 97.8% accuracy rate at detecting speech from non-speech. The start trim point predictions were very positive, with an average difference of -11.22s from gold standard data. End trim point predictions showed a much greater deviation, with an average difference of 145.16s from gold standard data. Discussions between the lecturer and students, after the lecture, was predominantly the reason for this discrepancy. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/29778 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:57.328Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/29778 Investigating audio classification to automate the trimming of recorded lectures Govender, Devandran Suleman, Hussein Information Technology With the demand for recorded lectures to be made available as soon as possible, the University of Cape Town (UCT) needs to find innovative ways of removing bottlenecks in lecture capture workflow and thereby improving turn-around times from capture to publication. UCT utilises Opencast, which is an open source system to manage all the steps in the lecture-capture process. One of the steps involves manual trimming of unwanted segments from the beginning and end of video before it is published. These segments generally contain student chatter. The trimming step of the lecture-capture process has been identified as a bottleneck due to its dependence on staff availability. In this study, we investigate the potential of audio classification to automate this step. A classification model was trained to detect 2 classes: speech and non-speech. Speech represents a single dominant voice, for example, the lecturer, and non-speech represents student chatter, silence and other environmental sounds. In conjunction with the classification model, the first and last instances of the speech class together with their timestamps are detected. These timestamps are used to predict the start and end trim points for the recorded lecture. The classification model achieved a 97.8% accuracy rate at detecting speech from non-speech. The start trim point predictions were very positive, with an average difference of -11.22s from gold standard data. End trim point predictions showed a much greater deviation, with an average difference of 145.16s from gold standard data. Discussions between the lecturer and students, after the lecture, was predominantly the reason for this discrepancy. 2019-02-22T11:53:48Z 2019-02-22T11:53:48Z 2018 2019-02-19T07:12:58Z Master Thesis Masters MSc http://hdl.handle.net/11427/29778 eng application/pdf Department of Computer Science Faculty of Science University of Cape Town |
| spellingShingle | Information Technology Govender, Devandran Investigating audio classification to automate the trimming of recorded lectures |
| thesis_degree_str | Master's |
| title | Investigating audio classification to automate the trimming of recorded lectures |
| title_full | Investigating audio classification to automate the trimming of recorded lectures |
| title_fullStr | Investigating audio classification to automate the trimming of recorded lectures |
| title_full_unstemmed | Investigating audio classification to automate the trimming of recorded lectures |
| title_short | Investigating audio classification to automate the trimming of recorded lectures |
| title_sort | investigating audio classification to automate the trimming of recorded lectures |
| topic | Information Technology |
| url | http://hdl.handle.net/11427/29778 |
| work_keys_str_mv | AT govenderdevandran investigatingaudioclassificationtoautomatethetrimmingofrecordedlectures |