Comparative Study on Reference Signal for Data Selection Algorithms in PEM-AFC Framework

In this paper, we explore an acoustic feedback cancellation (AFC) algorithm that integrates the correntropy-based data-selective algorithm within the prediction error method (PEM). The data selective approach compares the performance of using the error signal $e(n)$ and the pre-whitened error signal $e_{p}(n)$ as data samples. It is shown that using the error signal provides superior system performance and audio quality compared to the pre-whitened error signal, while closely approximating the performance of the original algorithm.