Stability-controlled hybrid adaptive feedback cancellation scheme for hearing AIDS
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© 2018 Crown. Adaptive feedback cancellation (AFC) techniques are common in modern hearing aid devices (HADs) since these techniques have been successful in increasing the stable gain. Accordingly, there has been a significant effort to improve AFC technology, especially for open-fitting and in-ear HADs, for which howling is more prevalent due to the large acoustic coupling between the loudspeaker and the microphone. In this paper, the authors propose a hybrid AFC (H-AFC) scheme that is able to shorten the time it takes to recover from howling. The proposed H-AFC scheme consists of a switched combination adaptive filter, which is controlled by a soft-clipping-based stability detector to select either the standard normalized least mean squares (NLMS) algorithm or the prediction-error-method (PEM) NLMS algorithm to update the adaptive filter. The standard NLMS algorithm is used to obtain fast convergence, while the PEM-NLMS algorithm is used to provide a low bias solution. This stability-controlled adaptation is hence the means to improve performance in terms of both convergence rate as well as misalignment, while only slightly increasing computational complexity. The proposed H-AFC scheme has been evaluated for both speech and music signals, resulting in a significantly improved convergence and re-convergence rate, i.e., a shorter howling period, as well as a lower average misalignment and a larger added stable gain compared to using either the NLMS or the PEM-NLMS algorithm alone. An objective evaluation using the perceptual evaluation of speech quality and the perceptual evaluation of audio quality measures shows that the proposed H-AFC scheme provides very high-quality speech and music signals. This has also been verified through a subjective listening experiment with N = 15 normal-hearing subjects using a multi-stimulus test with hidden reference and anchor, showing that the proposed H-AFC scheme results in a better perceptual quality than the state-of-the-art PEM-NLMS algorithm.
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