Statistical Audibility Prediction (SAP) Algorithm

About this technology

Overview

Noise is difficult to escape in our daily lives. Such noise is generated by transportation vehicles, industrial equipment, hospital machines, phones, alarms, crowds, and more. Some sounds we want to suppress (e.g., airplane noise vs. conversation) and others we want to enhance (e.g., our ringing phone vs. subway noise). 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 (LaRC) 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. The continued development of this algorithm could allow engineers to suppress how we hear noise relative to sounds of interest. SAP can be implemented either as software or hardware. The algorithm has been tested using subject response data gathered in the Exterior Effects Room (EER) at NASA LaRC.