Mean square performance evaluation in frequency domain for an improved adaptive feedback cancellation in hearing aids

Abstract We consider an adaptive linear prediction based feedback canceller for hearing aids that exploits two (an external and a shaped) noise signals for a bias-less adaptive estimation. In particular, the bias in the estimate of the feedback path is reduced by synthesizing the high-frequency spectrum of the reinforced signal using a shaped noise signal. Moreover, a second shaped (probe) noise signal is used to reduce the closed-loop signal correlation between the acoustic input and the loudspeaker signal at low frequencies. A power-transfer-function analysis of the system is provided, from which the effect of the system parameters and adaptive algorithms [normalized least mean square (NLMS) and recursive least square (RLS)] on the rate of convergence, the steady-state behaviour and the stability of the feedback canceller is explicitly found. The derived expressions are verified through computer simulations. It is found that, as compared to feedback canceller without probe noise, the cost of achieving an unbiased estimate of the feedback path using the feedback canceller with probe noise is a higher steady-state misadjustment for the RLS algorithm, whereas a slower convergence and a higher tracking error for the NLMS algorithm.