Feedback oscillation is one of the major issues with hearing aids. An effective way of feedback suppression is adaptive feedback cancellation, which uses an adaptive filter to estimate the feedback path. However, when the external input signal is correlated with the receiver input signal, the estimate of the feedback path is biased. This so-called “bias problem” results in a large modeling error and a cancellation of the desired signal. This paper proposes a band-limited linear predictive coding based approach to reduce the bias. The idea is to replace the hearing-aid output with a synthesized signal, which sounds perceptually the same as or similar to the original signal but is statistically uncorrelated with the external input signal at high frequencies where feedback oscillation usually occurs. Simulation results show that the proposed algorithm can effectively reduce the bias and the misalignment between the real and the estimated feedback path. When combined with filtered-X adaptation in the feedback canceller, this approach reduces the misalignment even further.

This paper proposes a reflection model for the dynamic feedback path of digital hearing aids and compares it with two existing models: a direct model and an initialization model, based on the measured dynamic feedback paths. The comparison shows that the proposed model is superior to the existing two models in terms of maximum stable gain (MSG). For hearing aids with dual microphones, the possibility of relating the two dynamic feedback paths is also investigated. It is shown that in a complicated acoustic environment, the relation between the two feedback paths can be very intricate and difficult to exploit in modelling the dynamic feedback paths.