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An Algorithm for Statistical Audibility Prediction (SAP) of an Arbitrary Signal in the Presence of Noise

A method for predicting the audibility of an arbitrary time-varying noise (signal) in the presence of masking noise is described. The statistical audibility prediction (SAP) method relies on the specific loudness, or loudness perceived through the individual auditory filters, for accurate statistical estimation of audibility vs. time. As such this work investigated a new hypothesis that audibility is more accurately discerned within individual auditory filters by a higher-level decision-making process. Audibility prediction vs. time is intuitive since it captures changes in audibility with time as it occurs, critical for the study of human response to noise. Concurrently time-frequency prediction of audibility may provide valuable information about the root cause(s) for audibility useful for the design and operation of sources of noise. Empirical data, gathered under a three-alternative forced-choice (3AFC) test paradigm for low-frequency sound, has been used to examine the accuracy of SAPs.

 

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Permalink: https://aes2.org/publications/elibrary-page/?id=21463


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