An Artificial neural network approach for predicting architectural speech security (L)

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TypeArticle
Journal titleJournal of the Acoustical Society of America
Volume117
IssueApril 4
Pages17091712; # of pages: 4
Subjectspeech privacy, neural networks; Speech security
AbstractSignal-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.
Publication date
LanguageEnglish
AffiliationNRC Institute for Research in Construction; National Research Council Canada
Peer reviewedYes
NRC number48322
17670
NPARC number20377600
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Record identifiere66ba208-8e75-4fd2-bf4e-eaa6676081d2
Record created2012-07-24
Record modified2016-05-09
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