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In hearing aids, acoustic feedback occurs due to the coupling between the hearing aid loudspeaker and microphone(s). In order to reduce the acoustic feedback, adaptive filters are commonly used to estimate the feedback contribution in the microphone(s). While theoretically allowing for perfect feedback cancellation, in practice the adaptive filter converges to an optimal solution that is typically biased due to the closed-loop acoustical system of the hearing aid. In order to avoid the adaptation to a biased optimal solution, in this paper we propose to use a fixed beamformer to cancel the acoustic feedback contribution 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. While previous null-steering beamforming approaches did not control for distortions of the incoming signal, in this paper we propose to incorporate a constraint based on the relative transfer function (RTF) of the incoming signal, aiming to perfectly preserve this signal. We formulate the computation of the beamformer coefficients both as a least-squares optimization procedure, aiming to minimize the residual feedback power, and as a min–max optimization procedure, aiming to directly maximize the maximum stable gain of the hearing aid. Experimental results using measured acoustic feedback paths from a custom earpiece with two microphones in the vent and a third microphone in the concha show that the proposed fixed null-steering beamformer using the RTF-based constraint provides a reduction of the acoustic feedback and substantially increases the added stable gain while preserving the incoming signal. This can even be achieved for unknown acoustic feedback paths and incoming signal directions.

Asparaginyl endopeptidases (AEPs) are cysteine proteases that control a myriad of cellular functions in plants, including maturation of seed storage proteins and programmed cell death. Recently, several noteworthy AEPs have been discovered that primarily function as transpeptidases rather than hydrolases, to instead catalyse the formation of new peptide bonds. These AEPs appear to have evolved for the cyclisation of a large class of plant defence peptides called cyclotides. Here we describe recent insights into the structural differences between AEPs that preference peptide ligation over hydrolysis. This knowledge is instrumental for the deployment of AEP ligases as biotechnological tools for in vitro applications such as protein labelling and or cyclization, and for plant molecular farming applications.

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.

Recently it has been proposed to use a fixed beamformer to cancel the acoustic feedback for a custom earpiece with multiple integrated microphones and loudspeakers. By steering a spatial null in the direction of the hearing aid loudspeaker theoretically perfect feedback cancellation can be achieved. In contrast to previous approaches that constrained the beamformer coefficients in a reference microphone to a simple delay, in this paper we use a constraint based on the relative transfer function of the incoming signal aiming to perfectly preserve the incoming signal. Experimental results using measured acoustic feedback paths from a custom earpiece with three microphones show that the proposed RTF constrained null- steering beamformer allows to substantially increase the added stable gain even for unknown acoustic feedback paths, e.g., with a telephone receiver close to the ear. Furthermore, it yields a high perceptual speech quality of the incoming signal even for unknown incoming signal directions.

In adaptive feedback cancellation (AFC) methods, the step-size plays an important role in controlling the convergence speed of an adaptive filter in the feedback canceller path. The selection of this step-size provides a compromise between a low steady-state error and a fast convergence rate. The use of a variable step-size (VSS) is a potential solution to achieve both fast convergence and low steady-state error. In this paper, we propose a frequency-domain AFC method which integrates an improved practical VSS (IPVSS) algorithm into a partitioned-block frequency-domain (PBFD) implementation of the prediction error method (PEM) for hearing aid applications. The proposed method derives benefit from the IPVSS algorithm, e.g., a better compromise solution between convergence and steady-state error, and from the PBFD-PEM, e.g., a low numerical complexity and an improved convergence. The proposed method is evaluated for different types of speech incoming signals as well as for a sudden change of the acoustic feedback path. Simulation results show that the proposed method provides a significant performance improvement compared to the PBFD affine combination approach as well as the PBFD using either the upper or the lower step-sizes which are utilised as boundaries in the IPVSS algorithm.

Numerous adaptive feedback cancellation (AFC) algorithms are used in open-fitting and in-ear hearing aid devices (HADs) in order to avoid the possible annoying howling sounds. Recently, a hybrid AFC (H-AFC) scheme that shortened the recovering time from howling was proposed. It consists of a switched combination adaptive filter controlled by a stability detector that chooses either the standard normalized least mean squares (NLMS) algorithm or the prediction-error method (PEM) NLMS algorithm. In this paper a hybrid simplified Kalman filter (H -SKF) that uses a modified stability detector and a switch between NLMS and (PEM) SKF algorithms is proposed. It is shown that the proposed approach improves the convergence properties and shortens the howling periods for both speech and music signals compared with the hybrid NLMS (H-NLMS) algorithm.

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 null-steering beamformer to reduce the acoustic feedback in the microphones. In this paper we propose to combine the fixed null-steering beamformer with an additional adaptive filter to cancel the residual feedback component in the beamformer output. We compare the combination of the fixed null-steering beamformer and different adaptive filtering algorithms including subband adaptive filtering and the prediction-error-method based fullband adaptive filtering with using either of the two approaches alone. Experimental results using measured acoustic feedback show the benefit of using the combined approach compared to using either of the two approaches to cancel the acoustic feedback.

The affine projection algorithm (APA) is commonly used for adaptive filtering in acoustic echo cancellation (AEC) due to its higher convergence and tracking rate compared to the conventional normalized least mean squares (NLMS) algorithm, especially for spectrally colored incoming signals. However, its application to adaptive feedback control (AFC) in hearing aids (HAs) is still not common because of the inherent correlation between the loudspeaker and incoming signals as well as the increase in computational complexity. In this paper, we investigate a way to employ a low projection order APA in conjunction with an improved practical variable step-size (IPVSS) for the prediction error method (PEM) based adaptive feedback control in HAs. The proposed approach is evaluated for a speech incoming signal and for a sudden change of the acoustic feedback path. The experimental results show that the proposed approach yields much better performance than a system employing either the upper or the lower fixed step-size used in the IPVSS as well as the PEM using the IPVSS for the NLMS algorithm (PEM- IPVSS-NLMS). By choosing a small projection order for the APA there is only a slight increase in the computational complexity. Moreover, a small amount of frequency shifting (FS) is integrated to further improve the system's performance.

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.

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).

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.

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.

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.