Predicting the extent that one sound is heard over another is difficult, yet could help engineers to better design for sound management. Innovators at the NASA Langley Research Center and the National Institute of Aerospace (NIA) have developed an algorithm for Statistical Audibility Prediction (SAP) of an arbitrary signal in the presence of noise. The SAP algorithm compares the loudness of signal and noise samples at matching time instances to assess audibility versus time.
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.
The algorithm has been tested using subject response data gathered in the Exterior Effects Room (EER) at NASA LARC. Future work should involve additional studies to examine the performance of SAP with realistic ambient noise and signals with higher-frequency content. The continued development of this algorithm could allow engineers to suppress how we hear noise relative to sounds of interest.
The technology has several potential applications including noise management, sound engineering, transportation vehicles (e.g., aircraft, rotorcraft, drones, unmanned aerial vehicles,trucks, automobiles, trains, buses, etc.), as well as communication devices (e.g., phones, alarms, etc.) and hearing aids.