Real-Time Perceptual Model for Distraction in Interfering Audio-on-Audio Scenarios

This letter proposes a real-time perceptual model predicting the experienced distraction occurring in interfering audio-on-audio situations. The proposed model improves the computational efficiency of a previous distraction model, which cannot provide predictions in real time. The chosen approach was to utilize similar features as the previous model, but to use faster underlying algorithms to calculate these features. The results show that the proposed model has a root mean squared error of 11.9%, compared to the previous model's 11.0%, while only taking 0.04% of the computational time of the previous model. Thus, while providing similar accuracy as the previous model, the proposed model can be run in real time. The proposed distraction model can be used as a tool for evaluating and optimizing sound-zone systems. Furthermore, the real-time capability of the model introduces new possibilities, such as adaptive sound-zone systems.