The computational prediction of masking thresholds for ecologically valid interference scenarios

Abstract

Auditory interference scenarios, where a listener wishes to attend to some target audio while being presented with interfering audio, are prevalent in daily life. The goal of developing an accurate computational model which can predict masking thresholds for such scenarios is still incomplete. While some sophisticated, physiologically inspired, masking prediction models exist, they are rarely tested with ecologically valid programs (such as music and speech). In order to test the accuracy of model predictions human listener data is required. To that end a masking threshold experiment was conducted for a variety of target and interferer programs. The results were analyzed alongside predictions made by the computational auditory signal processing and prediction model described by Jepsen et al. (2008). Masking thresholds were predicted to within 3 dB root mean squared error with the greatest prediction inaccuracies occurring in the presence of speech. These results are comparable to those of the model by Glasberg and Moore (2005) for predicting the audibility of time-varying sounds in the presence of background sounds, which otherwise represent the most accurate predictions of this type in the literature.