Estimation of the common part of acoustic feedback paths in hearing aids using iterative quadratic programming

In adaptive feedback cancellation the convergence speed and the computational complexity depend on the number of adaptive parameters used to model the acoustic feedback path. To improve the convergence speed and reduce the computational complexity, it has been proposed to model the acoustic feedback path as the convolution of a time-invariant common pole-zero part and a time-varying variable part. Previous approaches to estimate all the coefficients minimized the so-called equation-error which possibly suffers from poor estimation accuracy in the vicinity of prominent spectral regions, e.g., spectral peaks. In this paper we therefore propose to minimize the so-called output-error by using a Steiglitz-McBride-like iteration scheme. To ensure the stability of the estimated pole-zero filter a frequency domain constraint is used leading to a quadratic programming problem. Experimental results using measured impulse responses from a two-microphone behind-the-ear hearing aid show that the proposed estimation scheme outperforms the existing estimation scheme in terms of modeling accuracy.