Predicting Subjective Satisfaction with Speech Prediction-Based ANC Using Perceptually Relevant Metrics Correlated with Sound Attributes

The perceptual performance of active noise control (ANC) head-phones depends on various sound attributes that collectively influence end-user satisfaction. In the speech prediction-based ANC for headphone applications, two key attributes, the amount of speech and the amount of artifacts, significantly affect subjective satisfaction with the ANC experience. This study aims to identify objective metrics correlating with the perceptual assessment of these attributes to predict subjective satisfaction with the considered ANC systems. Such prediction can significantly simplify the design and evaluation process of the ANC systems, avoiding expensive and time-consuming subjective tests. By establishing the perceptual relevance of attenuation, a commonly used objective performance metric for ANC, to the amount of speech and utilizing conventional quality metrics, i.e., PEAQ, POLQA, and PESQ, for the amount of artifacts, we demonstrate high-quality fits when predicting these attributes. Our findings indicate that these metrics effectively predict subjective satisfaction with the considered ANC systems, as evidenced by a high explained variance, with an R-squared of 0.98 and a relatively low root mean square error ranging from 0.09 to 0.19 on a five-point scale.