An acoustic feedback canceller using probe noise and informative data

Abstract One of the important issues encountered in acoustic feedback canceller (AFC) systems is the bias occurring in the estimation of the acoustic feedback (AF) path. In the literature, a common solution is to inject the probe noise into the loudspeaker to reduce the correlation between the input signal and the loudspeaker signal, that is, to remove the bias occurring in the estimation of the AF path. This approach significantly improves the performance of the AFC systems. However, when considering high filter orders and large-scale input signals, such systems designed by probe noise injection suffer from the data processing cost, and thus their real-time implementations on a chip generally become challenging. To this end, in this paper, we propose an AFC system using jointly probe noise and informative data, named the probe noise-online censoring-least mean square (PN-OC-LMS) based AFC. Thanks to both the OC mechanism and the probe noise injection approach, the proposed PN-OC-LMS based AFC system significantly not only reduces the data processing cost but also effectively removes the bias effect in the AF path. The mentioned effectiveness of the proposed AFC system is supported by two comprehensive different experiments on a real-world AF path measured from a behind-the-ear hearing aid. As a result, in the proposed AFC system, the use of both informative data and the injection of probe noise will enable the design of a long-term working wearable hearing aid device that provide slow data processing cost and correctly estimates the AF path.