Linh Thi Thuc Tran

Improving adaptive feedback cancellation in hearing aids using an affine combination of filters

In adaptive feedback cancellation an adaptive filter is used to model the acoustic feedback path between the hearing aid loudspeaker and the microphone. An important parameter for adaptive filters is the step-size, providing a trade-off between fast convergence and low steady-state misalignment. In order to achieve both fast convergence as well as low steady-state misalignment, it has been proposed to use an affine combination scheme of two filters operating with different step-sizes. In this paper we apply such an affine combination scheme to the acoustic feedback cancellation problem in hearing aids. We show that for speech signals a time-domain affine combination scheme yields a biased solution. To reduce this bias we propose to use a partitioned-block frequency-domain affine combination scheme. Experimental results using measured acoustic feedback paths show that in terms of misalignment and added stable gain the proposed adaptive feedback cancellation system outperforms a system that only uses a single adaptive filter with either of the fixed step-sizes used for the affine combination scheme.

Acoustic Feedback Cancellation in hearing aids using two microphones employing variable step size affine projection algorithms

Affine projection algorithms (APA) have been widely employed for acoustic echo cancellation (AEC) since they provide a natural trade-off between convergence speed and computational complexity. However, their application in Acoustic Feedback Cancellation (AFC) in hearing aids so far has been limited due to lack of performance improvement in one microphone settings. A two microphone technique was recently proposed to provide an improved cancellation for a larger class of input signals. This paper proposes to use APA in the two microphone closed-loop feedback cancellation. It is shown that APA has significantly improved the misalignment and maximum stable gain of the system. Moreover, a new variable Gaussian step-size control (VGSS) for APA is also proposed. The simulation results indicate an improvement in convergence speed of the proposed algorithms as compared to previously suggested methods.

Affine projection algorithm for acoustic feedback cancellation using prediction error method in hearing aids

Prediction error method (PEM) is popularly applied to acoustic feedback cancellation (AFC) in hearing aids. Commonly, this method uses normalized least mean square (NLMS) adaptive filter to estimate the coefficients of the real feedback path. A disadvantage of NLMS algorithm is to provide a slow convergence rate when coloured incoming signals are used. To address this problem, we propose a simple but effective way to apply an affine projection algorithm (APA) to acoustic feedback cancellation using PEM. Performance of the proposed method is evaluated for speech incoming signal in both cases of using/not using a probe noise. Simulation results show that the proposed method outperforms the PEM using NLMS in both terms of misalignment and added stable gain.

Null-steering beamformer for acoustic feedback cancellation in a multi-microphone earpiece optimizing the maximum stable gain

Commonly adaptive filters are used to reduce the acoustic feedback in hearing aids. While theoretically allowing for perfect cancellation of the feedback signal, in practice the adaptive filter solution is typically biased due to the closed-loop hearing aid system. In contrast to conventional behind-the-ear hearing aids, in this paper we consider an earpiece with multiple integrated microphones. For such an earpiece it has previously been proposed to use a fixed beamformer to reduce the acoustic feedback in the microphones which has been designed to minimize a least-squares cost function. In this paper we propose to design the beamformer by minimizing a min-max cost function which directly maximizes the maximum stable gain of the earpiece. Furthermore, we propose a robust extension of the min-max cost function maximizing the worst-case maximum stable gain over a set of acoustic feedback paths. Experimental results using measured acoustic feedback paths show that the feedback cancellation performance of the fixed beamformer can be considerably improved by minimizing the proposed min-max optimization problem, while maintaining a high perceptual quality of the incoming signal.

Acoustic feedback cancellation for a multi-microphone earpiece based on a null-steering beamformer

In order to reduce acoustic feedback in hearing aids, adaptive filters are commonly used to estimate the feedback contribution in the microphone(s). While theoretically allowing for perfect feedback cancellation, in practice the solution is typically biased due to the closed-loop acoustical system. In this paper, we propose to use a fixed beamformer to cancel the acoustic feedback for an earpiece with multiple integrated microphones and loudspeakers. By steering a spatial null in the direction of the hearing aid loudspeaker we show that theoretically perfect feedback cancellation can be achieved. Experimental results using measured acoustic feedback paths from an earpiece with two microphones in the vent and a third microphone in the concha show that the proposed fixed beamformer provides a reduction of the acoustic feedback and substantially increases the added stable gain while maintaining a high perceptual speech quality even for unknown acoustic feedback paths, e.g., after repositioning of the earpiece or with a telephone receiver close to the ear.

Two-Microphone Hearing Aids Using Prediction Error Method for Adaptive Feedback Control

A challenge in hearing aids is adaptive feedback control which often uses an adaptive filter to estimate the feedback path. This estimate of the feedback path usually results in a bias due to the correlation between the loudspeaker signal and the incoming signal. The prediction error method (PEM) is a popular method for reducing this bias for adaptive feedback control (AFC) in hearing aids, providing a significant performance improvement compared to conventional adaptive feedback control techniques. However, the PEM-based AFC (PEM-AFC) applications are still limited to single-microphone single-loudspeaker (SMSL) systems. This paper investigates the application of the PEM-AFC to a two-microphone single-loudspeaker hearing aid with detailed theoretical analysis as well as practical experiments. In the proposed method, PEM-AFC2, we use the two-microphone adaptive feedback control (AFC2) method with two microphones and one loudspeaker. The incoming signals at the two microphones are related by a relative transfer function (RTF) which is used to predict the incoming signal at the main microphone. In addition, a prefilter is employed to prewhiten the loudspeaker and the microphone signals before the adaptive filter estimates. As a result, the proposed method obtains a lower bias and a faster tracking rate compared to the PEM-AFC and the AFC2 method, while still maintaining a good quality of the incoming signal. A new derivation for optimal filters in the AFC2 method will also be provided. The performance of the proposed method is evaluated for speech shaped noise as incoming signal and with undermodeling the RTF as well as with perfect modeling the RTF. Moreover, different types of incoming signals and a sudden change of feedback paths are also considered. The experimental results show that the proposed approach yields a significant performance improvement compared to existing state-of-the-art AFC methods such as the PEM-AFC and the AFC2.

Proportionate NLMS for adaptive feedback control in hearing aids

The proportionate normalized least-mean-squares (PNLMS) algorithm is commonly used in acoustic echo cancellation (AEC) context. It provides faster initial convergence and tracking rates compared to the NLMS algorithm for the case of sparse echo impulse responses. The improved PNLMS algorithm (IPNLMS) has been proven to be more powerful than PNLMS by exploiting new rules for computing the weight of each step-size corresponding to each adaptive filter coefficient. However, the application of the PNLMS and the IPNLMS algorithms for adaptive feedback control (AFC) in hearing aids (HAs) is still limited due to high correlation between the loudspeaker and incoming signals. This paper proposes implementations of the PNLMS/IPNLMS algorithms for AFC using the prediction error method (PEM) for hearing aids. The proposed methods have been evaluated for both speech and music incoming signals. Simulation shows that the proposed methods have faster initial convergence and tracking than the PEM using the NLMS algorithm (PEM-NLMS).

Improved practical variable step-size algorithm for adaptive feedback control in hearing aids

Variable step-size (VSS) schemes are popular to use in acoustic echo cancellation (AEC) contexts. However, their effective implementation in adaptive feedback cancellation (AFC) for hearing aids is still challenging due to the correlation between microphone and loudspeaker signals. We propose an improved practical VSS algorithm (IPVSS) which uses a variable step-size with upper and lower limits to control the update equation of an adaptive filter. The proposed algorithm is implemented for feedback cancellation using the prediction error method. As a result, the overall system has a fast convergence rate, a high tracking rate and a low steady-state error. The performance of the proposed approach has been evaluated for both speech and music incoming signals. The simulation results show that the proposed approach outperforms a system which only utilizes either the lower or the upper fixed step-size used in the IPVSS.

Stability-controlled hybrid adaptive feedback cancellation scheme for hearing aids

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.

Two-microphone acoustic feedback cancellation using NLMS based algorithms

In this paper a sign NLMS algorithm variant is proposed to adapt the second microphone on the two microphone approach in hearing aids. for closed-loop feedback cancellation. The simulation results show improved convergence properties over competing methods used to tackle the acoustic feedback problem.