Individual-activation-factor based novel approach for acoustic feedback suppression in hearing aid

The conventional adaptive filtering algorithms do not perform satisfactorily while modeling sparse impulse response of the feedback path of hearing aid in the presence of real input signals which are colored in nature. Furthermore, the high computational overhead of these adaptive feedback cancelers (AFC), make the situation difficult for a real-time, power and area constrained device like hearing aid. In an endeavor, to overcome these limitations, a de-correlated memory proportionate affine projection-like algorithm (DMPAPL) with individual-activation-factor (IAF-DMPAPL) is proposed for improved acoustic feedback cancellation in hearing aids. The concept of individual activation factor results in better distribution of the adaptation energy over the adaptive filter taps while estimating sparse impulse response. An adaptive de-correlation filter along with memory of previous proportionate gain factor employed to affine-projection-like algorithm offers improved modeling accuracy along with reduced computational cost. The corresponding update rule and bound on the learning rate of the proposed feedback cancellation system has been derived. Extensive simulation study demonstrates the efficacy of the proposed AFC over other methods for various real input signals in case of different feedback path scenarios.