| Download | - View accepted manuscript: An Artificial neural network approach for predicting architectural speech security (L) (PDF, 736 KiB)
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| Author | Search for: Xu, J.; Search for: Bradley, J. S.1; Search for: Gover, B. N.1 |
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| Affiliation | - National Research Council of Canada. NRC Institute for Research in Construction
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| Format | Text, Article |
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| Subject | speech privacy, neural networks; Speech security |
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| Abstract | Signal-to-noise (S/N) type measures have been developed for predicting architectural speech privacy and speech security, which is required to accurately rate the probability of a listener outside a room being able to overhear conversations from within the room. However, these measures may not be ideal for speech security situations. In the present work, an approach that uses the artificial neural networks to directly represent the functional relationship between the octave band (250 Hz ? 8 kHz) S/N ratios and the speech intelligibility score and security thresholds has been investigated. The artificial neural network (ANN) approach provides a direct and accurate method for predicting the speech intelligibility score and security thresholds. |
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| Publication date | 2005-04-01 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NRC number | NRCC 48322
NRC-IRC-17670 |
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| NPARC number | 20377600 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | e66ba208-8e75-4fd2-bf4e-eaa6676081d2 |
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| Record created | 2012-07-24 |
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| Record modified | 2020-04-07 |
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