Unifying background models over complex audio using entropy
In this paper we extend an existing audio background modelling technique, leading to a more robust application to complex audio environments. The determination of background audio is used as an initial stage in the analysis of audio for surveillance and monitoring applications. Knowledge of the back...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Coputer Society Conference Publishing Services
2006
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| Online Access: | http://hdl.handle.net/20.500.11937/42503 |
| _version_ | 1848756437555085312 |
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| author | Moncrieff, Simon Venkatesh, Svetha West, Geoffrey |
| author2 | Tang, Y. |
| author_facet | Tang, Y. Moncrieff, Simon Venkatesh, Svetha West, Geoffrey |
| author_sort | Moncrieff, Simon |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper we extend an existing audio background modelling technique, leading to a more robust application to complex audio environments. The determination of background audio is used as an initial stage in the analysis of audio for surveillance and monitoring applications. Knowledge of the background serves to highlight unusual or infrequent sounds. An existing modelling approach uses an online, adaptive Gaussian mixture model technique that uses multiple distributions to model variations in the background. The method used to determine the background distributions of the GMM leads to a failure mode of the existing technique when applied to complex audio. We propose a method incorporating further information, the proximity of distributions determined using entropy, to determine a more complete background model. The method was successful in more robustly modelling the background for complex audio scenes |
| first_indexed | 2025-11-14T09:12:11Z |
| format | Conference Paper |
| id | curtin-20.500.11937-42503 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:12:11Z |
| publishDate | 2006 |
| publisher | IEEE Coputer Society Conference Publishing Services |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-425032023-02-27T07:34:29Z Unifying background models over complex audio using entropy Moncrieff, Simon Venkatesh, Svetha West, Geoffrey Tang, Y. Wang, S. Lorette, G. Young, D. Yang, H. In this paper we extend an existing audio background modelling technique, leading to a more robust application to complex audio environments. The determination of background audio is used as an initial stage in the analysis of audio for surveillance and monitoring applications. Knowledge of the background serves to highlight unusual or infrequent sounds. An existing modelling approach uses an online, adaptive Gaussian mixture model technique that uses multiple distributions to model variations in the background. The method used to determine the background distributions of the GMM leads to a failure mode of the existing technique when applied to complex audio. We propose a method incorporating further information, the proximity of distributions determined using entropy, to determine a more complete background model. The method was successful in more robustly modelling the background for complex audio scenes 2006 Conference Paper http://hdl.handle.net/20.500.11937/42503 10.1109/ICPR.2006.1141 IEEE Coputer Society Conference Publishing Services fulltext |
| spellingShingle | Moncrieff, Simon Venkatesh, Svetha West, Geoffrey Unifying background models over complex audio using entropy |
| title | Unifying background models over complex audio using entropy |
| title_full | Unifying background models over complex audio using entropy |
| title_fullStr | Unifying background models over complex audio using entropy |
| title_full_unstemmed | Unifying background models over complex audio using entropy |
| title_short | Unifying background models over complex audio using entropy |
| title_sort | unifying background models over complex audio using entropy |
| url | http://hdl.handle.net/20.500.11937/42503 |