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 has been developed. 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. More recent development has focused on derivation and inclusion of frequency-dependent correction factors in SAP’s model to account for the ability to hear signals below the level of the masking noise. Audibility prediction vs. time is intuitive since it captures changes in audibility with time as it occurs, which is critical for the study of human response to noise. Concurrently time-frequency prediction of audibility may also 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 SAP.