Subjective and Objective Sound-Quality Evaluation of Adaptive Feedback Cancellation Algorithms

Abstract

Objective measures are widely used for the perceptual sound-quality evaluation of audio signal processing algorithms. Nevertheless, the use of subjective-evaluation measures remains relevant, in particular when application-specific objective measures are lacking. In this paper, we present a perceptual sound-quality evaluation of different algorithms for adaptive feedback cancellation (AFC), with both speech and music signals. Three algorithms are compared: the block normalized least mean squares algorithm, the prediction-error method (PEM) based frequency-domain adaptive filter, and the PEM-based frequency-domain Kalman filter (PEM-FDKF). The subjective evaluation results for the tested algorithms suggest that there is a large difference in statistical significance, and a corresponding large effect size, between the PEM-FDKF and the other algorithms, when using speech signals. A smaller statistical significance, and a lower effect size, is reported when using music signals. The subjective evaluation results are then compared with the results obtained with several objective measures. The correlation between subjective and objective scores shows that objective measures can be effectively used to predict the sound-quality degradation caused by acoustic feedback and AFC artifacts.