Adaptive feedback cancellation in hearing aids with linear prediction of the desired signal

Abstract

The standard continuous adaptation feedback cancellation algorithm for feedback suppression in hearing aids suffers from a large model error or bias if the received sound signal is spectrally colored. To reduce the bias in the feedback path estimate, we propose adaptive feedback cancellation techniques that are based on a closed-loop identification of the feedback path as well as the (auto-regressive) modeling of the desired signal. In general, both models are not simultaneously identifiable in the closed-loop system at hand. We show that-under certain conditions, e.g., if a delay is inserted in the forward path-identification of both models is indeed possible. Two classes of adaptive procedures for identifying the desired signal model and the feedback path are derived: a two-channel identification method as well as a prediction error method. In contrast to the two-channel identification method, the prediction error method allows use of different adaptation schemes for the feedback path and for the desired signal model and, hence, is found to be preferable for highly nonstationary sound signals. Simulation results demonstrate that the proposed techniques outperform the standard continuous adaptation algorithm if the conditions for identifiability are satisfied.

Adaptive Feedback Cancellation With Band-Limited LPC Vocoder in Digital Hearing Aids

“…The term in (11) represents the bias of the estimate, which is related to the correlation between the desired input signal and the processed hearing-aid signal . The magnitude of the bias depends strongly on the decaying speed of the autocorrelation function of , the forward-path delay, and nonlinearity in the hearing-aid process .…”

“…It also removes the long-term bias in the adaptation completely since is uncorrelated with [cf. (11)]. However, as mentioned in Section II, voiced speech is synthesized with an impulse train.…”

“…Since the two ADFs use the same filter , the phase misalignment between these two filters is zero and therefore the requirement of phase misalignment for stable adaptation [20] of the filtered-X algorithm is always satisfied. However, due to the group delay associated with the ADFs, the filtered-X algorithm may become unstable if the coefficients of the estimated feedback path change too fast [11].…”

“…A recently proposed decorrelation method exploits closed-loop identification techniques [9]- [11]. In [11], it has been proven that by minimizing the prediction error of the microphone signal, the estimate of the feedback path is not biased (identifiable) when the desired input signal is an autoregressive (AR) random process and when certain conditions are met.…”

“…In [11], it has been proven that by minimizing the prediction error of the microphone signal, the estimate of the feedback path is not biased (identifiable) when the desired input signal is an autoregressive (AR) random process and when certain conditions are met. A prediction error method-based adaptive feedback cancellation (PEM-AFC) is proposed in [11] to identify the models for the desired signal and the feedback path simultaneously. However, in practice, many desired input signals, such as voiced speech and music, are not AR processes.…”