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Acoustic coupling between microphone and loudspeaker is a significant problem in open-fit digital hearing aids. An open-fit compared to a close-fit hearing aid significantly lowers the signal quality and limits the achievable maximum stable gain. Adaptive feedback cancellation (AFC) enables an efficient approach to reduce the impact of acoustic coupling. However, without careful consideration, it can also introduce bias in estimating the feedback path due to the high correlation between the loudspeaker signal and the incoming signal, especially when the incoming signal is spectrally coloured, e.g., speech and music. The prediction error method (PEM) is well known for reducing this bias. The presented study aims to propose a switched PEM with soft-clipping (swPEMSC) that allows for further improvement in convergence/tracking rates, resulting in a better ability to recover from unstable/howling status. This swPEMSC employs a new update rule inspired by a soft-clipping based stability detector (SCSD). It allows to pick up either the PEMSC-NLMS or PEMSC-APA depending on the magnitude of the effective feedback signal, howling corresponds to a large feedback signal. The PEMSC-NLMS with a small step-size ensures a low steady-state error, but slow convergence/tracking rates, while PEMSC-APA with a large step-size allows for fast convergence/tracking rates, but a high steady-state error. By combining those approaches, the proposed approach can take advantage of good characteristics from both. Experimental results using different types of incoming signals and an abrupt change of feedback paths show that the swPEMSC can shorten unstable periods (howling) by improving the convergence and tracking rates while retaining a low steady-state error and good signal quality.

Acoustic feedback is a persistent problem in hearing aids, which limits the achievable amplification and may severely degrade the sound quality by producing howling artifacts. A potential approach to feedback cancellation is to estimate the feedback path utilizing an adaptive filter. However, estimation of the feedback path suffers a large model error, known as the bias, due to the correlation between the loudspeaker and source signals. A prediction-error method (PEM) based prewhitening filter has been widely utilized to reduce the bias. This approach, however, requires a large number of adaptive parameters, thus increasing the computational complexity, reducing the convergence rate, and limiting the added stable gain. We introduce an adaptive feedback cancellation (AFC) algorithm derived based on the orthonormal basis functions (OBFs) for closed-loop identification of the feedback path by minimizing the prediction error. The OBFs are defined by a set of fixed poles and a small number of adaptive tap-output weights. We study two methods for obtaining the fixed poles, an inherently stable least-squares method and a log-scale frequency resolution method. The poles are then embedded as the a priori information into the algorithm. The proposed algorithm is extensively evaluated with speech and music source signals and with sudden changes in the feedback path. The experimental results show that the proposed method significantly increases the added stable gain, accelerates the convergence rate, and enhances the sound quality compared to state-of-the-art, while requiring far fewer adaptive parameters which leads to reduced computational complexity.

Adaptive feedback cancellation (AFC) method is widely adopted for the purpose of reducing the adverse effects of acoustic feedback on the sound reinforcement systems. However, since the existence of forward path results in the correlation between the source signal and the feedback signal, the source signal is mistakenly considered as the feedback signal to be eliminated by adaptive filter when it is colored, which leads to a inaccurate prediction of the acoustic feedback signal. In order to solve this problem, prediction error method is introduced in this paper to remove the correlation between the source signal and the feedback signal. Aiming at the dilemma of Modified Least Mean Square (MLMS) algorithm in choosing between prediction speed and prediction accuracy, an improved MLMS algorithm with a variable step-size scheme is proposed. Simulation examples are applied to show that the proposed algorithm can obtain more accurate prediction of acoustic feedback signal in a shorter time than the MLMS algorithm.

We propose a new adaptive feedback cancellation (AFC) system in hearing aids (HAs) based on a well-posed optimization criterion that jointly considers both decorrelation of the signals and sparsity of the underlying channel. We show that the least squares criterion on subband errors regularized by a p-norm-like diversity measure can be used to simultaneously decorrelate the speech signals and exploit sparsity of the acoustic feedback path impulse response. Compared with traditional subband adaptive filters that are not appropriate for incorporating sparsity due to shorter sub-filters, our proposed framework is suitable for promoting sparse characteristics, as the update rule utilizing subband information actually operates in the fullband. Simulation results show that the normalized misalignment, added stable gain, and other objective metrics of the AFC are significantly improved by choosing a proper sparsity promoting factor and a suitable number of subbands. More importantly, the results indicate that the benefits of subband decomposition and sparsity promoting are complementary and additive for AFC in HAs.

© 2014 IEEE. Objective measures are widely used for the perceptual sound-quality evaluation of audio signal processing algorithms. Nevertheless, the use of subjective-evaluation measures remains relevant, in particular when application-specific objective measures are lacking. In this paper, we present a perceptual sound-quality evaluation of different algorithms for adaptive feedback cancellation (AFC), with both speech and music signals. Three algorithms are compared: The block normalized least mean squares algorithm, the prediction-error method (PEM) based frequency-domain adaptive filter, and the PEM-based frequency-domain Kalman filter (PEM-FDKF). The subjective evaluation results for the tested algorithms suggest that there is a large difference in statistical significance, and a corresponding large effect size, between the PEM-FDKF and the other algorithms, when using speech signals. A smaller statistical significance, and a lower effect size, is reported when using music signals. The subjective evaluation results are then compared with the results obtained with several objective measures. The correlation between subjective and objective scores shows that objective measures can be effectively used to predict the sound-quality degradation caused by acoustic feedback and AFC artifacts. ispartof: IEEE Transactions on Audio Speech and Language Processing vol:26 issue:5 pages:1010-1024 status: published

In adaptive feedback cancellation, the feedback path must be modeled precisely using as few adaptive parameters as possible to reduce computational complexity and enable rapid convergence. To reduce the number of adaptive parameters, the feedback path is usually modeled with a transfer function composed of an invariant component and a variable component using all-pole, all-zero, and pole-zero filters. These filters may be inefficient, requiring a large number of parameters for their specification, particularly in reverberant environments. In this letter, we present a weighted least-squares algorithm to precisely estimate the common poles of feedback paths, and we then model the invariant basis functions employing a Kautz filter. The Kautz filter is defined by a set of the fixed poles and a corresponding set of tap output weights. The fixed poles are associated with prominent peaks that are common to the measured feedback path frequency responses. The algorithm guarantees unconditionally the stability of the estimated poles of the inferred model. The experimental results using measured acoustic feedback paths from a two-microphone, behind-the-ear hearing aid show that the proposed method provides an accurate model of the feedback paths for a variety of acoustic environments that were not employed in estimating the common poles.

Modern-day digital hearing aids are prone to an unavoidable acoustic feedback phenomenon, degrading sound quality and speech intelligibility. The linear adaptive feedback cancellation (AFC) systems based on finite-impulse-response filters do not yield satisfactory performance under nonlinearity encountered in the feedback path. In an endeavor to overcome this, a de-correlated improved adaptive exponential functional link adaptive filter (DI-AEF)-based nonlinear AFC (NAFC) system is developed in this paper. It utilizes cross terms of the input samples and trigonometrical expansion with exponentially varying amplitude. To save computations, the delayed outputs of the feedback canceler are appended at the input layer, inspired by the IIR filtering technique. The adaptive de-correlation filter is updated by variable convergence and forgetting factor windowed recursive least square algorithm to address the biased estimation problem. The corresponding update rules, convergence, and bounded-input bounded-output stability conditions have been derived. Extensive simulation results demonstrate the efficacy of the proposed NAFC system for real input signals in terms of perceptual evaluation of speech and audio quality and added stable gain (ASG). The DI-AEF-based system achieves nearly 4-dB improvement in ASG while consuming 53% and 37% less multiplications and additions than the existing nonlinear methods.

The least mean square (LMS) based linear adaptive algorithms are commonly used for acoustic feedback cancellation (AFC) in digital hearing aids, given the simplicity, tracking capability, and robustness. Simultaneously, the LMS adaptive filter’s major limitation is slow convergence due to a compromised step-size selection. In recent times, the convex combination of LMS adaptive filter has depicted an improved trade-off between the convergence rate and steady-state while employed for adaptive feedback cancellation. However, there exists a scope for improving the convergence further for a time-varying feedback path under different noise conditions and input signals. The convex combination investigated with the mixing parameter δ n is confined to the (0,1) interval. Here we observed that the convex combination of two adaptive filters estimates the undesired feedback path dependently. Therefore, the optimal convex combination coefficients occur as a sequence that mitigates the mean square error (MSE). The proposed adaptive feedback cancellation framework has been analysed statistically for convex optimization cost-function. Simulation and results present the improvements of the proposed framework in comparison to the existing state-of-the-art.

Adaptive filtering based feedback cancellation is a widespread approach to acoustic feedback control. However, traditional adaptive filtering algorithms have to be modified in order to work satisfactorily in a closed-loop scenario. In particular, the undesired signal correlation between the loudspeaker signal and the source signal in a closed-loop scenario is one of the major problems to address when using adaptive filters for feedback cancellation. Slow convergence speed and limited tracking capabilities are other important limitations to be considered. Additionally, computationally expensive algorithms as well as long delays should be avoided, for instance, in hearing aid applications, because of power constraints, important to extend battery life, and real-time implementations requirements, respectively. We present an algorithm combining good decorrelation properties, by means of the prediction-error method based signal prewhitening, fast convergence, good tracking behavior, and low computational complexity by means of the frequency-domain Kalman filter, and low delay by means of a partitioned-block implementation.

138 p. Acoustic feedback is one of the challenges in hearing instrument design where the amount of acoustic gain of the hearing instrument is limited before the onset of acoustic feedback. When acoustic feedback occurs, the hearing instrument is effectively inoperable. Adaptive feedback cancellation algorithms embodied in digital hearing instruments is the method often adopted by manufacturers to address the acoustic feedback problem. MASTER OF ENGINEERING (EEE)

Modern-day hearing aids are capable of receiving acoustic signals over a wireless link and also from the surroundings through the microphone. If the hearing aid receives input only from the acoustic environment, feedback cancellation proceeds according to the existing methodologies for bias reduction. However, the wirelessly received signal and the acoustic environment input, when emitted from the same source, can be very similar to each other or with a time-delayed version of each other, thereby having a high correlation between them. Both inputs can also be emitted from different sources and, thus, be less correlated with each other. In the aforementioned scenarios, acoustic confusion can occur for the user as the hearing aid receives both signals simultaneously. To improve the output signal quality and to reduce bias in an adaptive feedback cancellation system with a wirelessly received signal as well as an acoustic environment input, we propose a cost function, and the optimization of the feed-forward path and of the shaping filter for the wireless signal. The feed-forward path is designed to be a cascade of the required acoustic enhancement along with an FIR filter. We derive expressions for an optimum shaping filter and for an optimized feed-forward path. Improvement in loudspeaker output signal quality, normalized misalignment and maximum stable gain for each of the above-mentioned scenarios is assessed through numerical simulations.

We consider an adaptive linear prediction based feedback canceller for hearing aids that exploits two (an external and a shaped) noise signals for a bias-less adaptive estimation. In particular, the bias in the estimate of the feedback path is reduced by synthesizing the high-frequency spectrum of the reinforced signal using a shaped noise signal. Moreover, a second shaped (probe) noise signal is used to reduce the closed-loop signal correlation between the acoustic input and the loudspeaker signal at low frequencies. A power-transfer-function analysis of the system is provided, from which the effect of the system parameters and adaptive algorithms [normalized least mean square (NLMS) and recursive least square (RLS)] on the rate of convergence, the steady-state behaviour and the stability of the feedback canceller is explicitly found. The derived expressions are verified through computer simulations. It is found that, as compared to feedback canceller without probe noise, the cost of achieving an unbiased estimate of the feedback path using the feedback canceller with probe noise is a higher steady-state misadjustment for the RLS algorithm, whereas a slower convergence and a higher tracking error for the NLMS algorithm.

In an adaptive feedback cancellation (AFC) scenario, it is essential for an algorithm to track and cancel the feedback signal as quickly as possible. We analyze typical feedback paths in hearing aids and show that they exhibit a low-rank nature. Further, to exploit this knowledge and improve the convergence and tracking performance for AFC, we propose the nearest Kronecker product decomposition based adaptive feedback canceller with prediction error method based signal pre-whitening. Detailed simulation study and comparison of computational complexity show that the proposed algorithm can provide improved convergence and tracking along with improved output speech quality over traditional AFC algorithms, at a moderate computational load.

This paper introduces a new adaptive feedback cancellation technique for hearing aid employing robust upper error bound of set membership filtering in the M-estimate function and applied for affine-projection-like algorithm. Additionally a l 1 norm based sparsity term is appended in the cost function which takes care of the convergence while estimating feedback path of the hearing aid exhibiting sparse characteristics. The proposed method of sparsity aware affine-projection-like robust set membership M-estimate (SAPL-RSM) filtering has been utilized for alleviating the impact of impulsive noise on the adaptation of feedback canceler’s weights. The SAPL-RSM technique is derived by minimizing a modified cost function involving M-estimate function constrained by a robust error bound and sparsity dependent penalty term with the motive of enhancing the convergence and lowering the computational expenses involved in the weight update process. The validation of the proposed method is carried out in the context of adaptive feedback cancellation in hearing aids. Simulations assess the efficacy of the proposed technique as an adaptive feedback canceler in terms of misalignment, added stable gain and sound quality deliverable at the user end.

In adaptive feedback cancellation both the convergence speed and the computational complexity depend on the number of adaptive parameters used to model the acoustic feedback paths. To reduce the number of adaptive parameters, it has been proposed to model the acoustic feedback paths as the convolution of a time-invariant common pole-zero filter and time-varying all-zero filters, enabling to track fast changes. In this paper, a novel procedure to estimate the common pole-zero filter of acoustic feedback paths is presented. In contrast to previous approaches which minimize the so-called equation-error, we propose to approximate the desired output-error minimization by employing a weighted least-squares procedure motivated by the Steiglitz--McBride iteration. The estimation of the common pole-zero filter is formulated as a semidefinite programming problem, to which a constraint based on the Lyapunov theory is added in order to guarantee the stability of the estimated pole-zero filter. Experimental results using measured acoustic feedback paths from a two microphone behind-the-ear hearing aid show that the proposed optimization procedure using the Lyapunov constraint outperforms existing optimization procedures in terms of modelling accuracy and added stable gain.

Acoustic feedback is a well-known phenomenon in hearing aids. Under certain conditions, it causes the so-called howling effect, which is highly annoying for the hearing aid user and limits the maximum amplification of the hearing aid. The standard adaptive feedback cancellation algorithms suffer from a biased adaptation if the input signal is spectrally colored or tonal, as it is for speech and music signals. Due to this bias distortion artifacts (entrainment) are generated. In this letter, we present a method to detect tonal, high correlated parts of the input signal. In particular, the method is able to distinguish between correlation resulting from the input signal and from feedback path changes. A subband feedback cancellation system which applies decorrelation methods is the basis for the proposed method. Additionally, we suggest to use the correlation detection to increase the performance of the mentioned feedback cancellation system. The performance is measured by preventing entrainment and reacting to feedback path changes.

Hearing aid users suffer from howling sound caused by acoustic coupling between the loudspeaker and the microphone(s) of this device. It is crucial to detect and eliminate the howling before it causes serious irritation to the hearing aid user. This study presents a multiple-feature method which uses voice activity detection (VAD) algorithm to reduce false alarm probability. Experimental results compare the performance of the proposed method with three conventional howling detection techniques in terms of detection probability, false alarm probability, and computational complexity. The proposed method possesses lower false alarm probability and less computational complexity compared to the other methods. This study proposes a frame-based howling detection method.The method uses a VAD algorithm in the pre-processing step.Applying VAD reduces computational complexity and false alarm probability.Multiple features of the howling are checked to increase the accuracy.

The convergence speed and the computational complexity of adaptive feedback cancellation algorithms both depend on the number of adaptive parameters used to model the acoustic feedback paths. To reduce the number of adaptive parameters it has been proposed to decompose the acoustic feedback paths as the convolution of a time-invariant common part and time-varying variable parts. Instead of estimating all parameters of the common and variable parts by minimizing the misalignment using a least-squares cost function, in this paper we propose to formulate the parameter estimation problem as a min-max optimization problem aiming to maximize the maximum stable gain (MSG). We formulate the min-max optimization problem as a semidefinite program and use a constraint based on Lyapunov theory to guarantee stability of the estimated common pole-zero filter. Experimental results using measured acoustic feedback paths show that the proposed min-max optimization outperforms least-squares optimization in terms of the MSG. Furthermore, the results indicate that the proposed common part decomposition is able to increase the MSG and reduce the number of variable part parameters even for unknown feedback paths that were not included in the optimization. Simulation results using an adaptive feedback cancellation algorithm based on the prediction-error-method show that the convergence speed can be increased by using the proposed feedback path decomposition.

Train public address (PA) system is an important component of Passenger Information System. Acoustic feedback is a well-known problem in public address systems. It will cause howling under certain conditions, which can severely degrade sound quality and limit the maximum allowable amplification of the PA systems. The general adaptive feedback cancellation (AFC) algorithms lead to a biased estimation because of the correlation between source and feedback signals. In order to solve the problem and achieve both fast convergence and low steady-state misalignment, a novel variable step-size improved proportionate affine projection algorithm (IPAPA) based on mixed error cost function with prediction error method (PEM) and howling suppression is proposed in this paper. The experimental results demonstrate the effectiveness of the proposed algorithm.

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.

Adaptive feedback cancellers (AFCs) based on normalized subband adaptive filtering (NSAF) exploiting the proportionate adaptation technique (PNSAF) have been recently reported for alleviating the feedback effect. The PNSAF algorithm exhibits faster convergence at the initial stage of adaptation whereas stagnating the convergence at later phase of the estimation process. An attempt has been made in this work to maintain the AFC convergence and misalignment in such conditions by introducing a penalty element in the cost function of PNSAF algorithm obtained from $l_{1}$ -norm of the coefficients. This results in inclusion of the zero attracting term during weight update mechanism and hence the proposed algorithm is termed as zero attracting PNSAF (ZA-PNSAF). Furthermore, prediction error method is utilized to reduce the bias related issues encountered in adaptive feedback cancellation for hearing aids. The derivations and convergence analysis of the proposed algorithm have been carried out. Simulation results demonstrate the efficacy of the proposed feedback cancellation method as compared to existing techniques using speech segments as input signal. The proposed ZA-PNSAF based AFC enables 3.5 dB more lowering of misalignment value in comparison to other algorithms while maintaining faster convergence.

Adaptive Feedback Cancellation (AFC) techniques are widely used to eliminate the undesired acoustic feedback effect arising in the Hearing Aid Devices (HADs) due to the coupling between the speaker and the microphone of the HAD. This paper proposes a method to eliminate the acoustic feedback effect in the HADs in presence of noisy environment. The method involves utilization of a computationally efficient Spectral Flux feature-based voice activity detector (VAD), which controls the process of Noise Injection in the proposed AFC algorithm (SFNIAFC). The proposed algorithm's performance is objectively evaluated using Misalignment (MISA) and Perceptual Evaluation of Speech Quality (PESQ) criteria for realistic noisy conditions. The simulations performed for the proposed method shows faster convergence and reduction in the MISA values with high PESQ values in comparison to the earlier method. Subjective test results support the effectiveness and better performance of the proposed algorithm for the HAD applications over earlier method.

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.

Acoustic feedback is a well-known phenomenon in hearing aids. Under certain conditions it causes the so-called howling effect, which is highly annoying for the hearing aid user and limits the maximum amplification of the hearing aid. The standard adaptive feedback cancellation algorithms suffer from a biased adaptation if the input signal is spectrally colored, as it is for speech and music signals. Due to this bias distortion artifacts (entrainment) are generated. In this paper, we present a sub-band feedback cancellation system which combines decorrelation methods with a new realization of a known non-parametric variable step size. To apply this step size in the context of adaptive feedback cancellation, a method to estimate the signal power of the desired input signal, i.e., without feedback, is necessary. A major part of this paper is spent with the theoretical derivation of this estimate. Furthermore, the complete system is evaluated extensively for several speech and music signals as well as for different feedback scenarios in simulations with feedback paths measured in concrete applications as well as for real-time simulations with hearing aid dummies. Both use hearing loss compensation methods as applied in physical hearing aids. The performance is measured in terms of being able to prevent entrainment and to react to feedback path changes. For both simulation setups the system shows a good performance with respect to the two performance measures. Furthermore, the overall feedback cancellation method relies only on few parameters, shows a low computational complexity, and therefore has a strong practical relevance.

In this contribution we describe an adaptive feed-back cancellation (FBC) system realized with 48 sub-band filters. As core procedure we propose a combination of two decorrelation measures to stabilize and optimally control the adaptation. We show that especially this combination of pre-whitening and frequency shift allows realizing three major steps for a fast and reliable FBC in real hearing aids. First, the adaptation bias is removed. Second, an optimal adaptation control can be realized, and third, we show that a differentiation between feedback and tonal input signals is possible. The latter can be used for an additional improvement of the adaptation control.

In hearing aids (HAs), the acoustic coupling between the microphone and the receiver results in the system becoming unstable under certain conditions and causes artifacts commonly referred to as whistling or howling. The least mean square (LMS) class of algorithms is commonly used to mitigate this by providing adaptive feedback cancellation (AFC). The speech quality after AFC and the amount of added stable gain (ASG) with AFC are used to assess these algorithms. In this paper, we introduce a variant of the LMS that promotes sparsity in estimating the acoustic feedback path. By using the l p norm as a diversity measure, the approach does not enforce, but takes advantage of sparsity when it exists. The performance in terms of speech quality, misalignment, and ASG of the proposed algorithm is compared with other proportionate-type LMS algorithms which also leverage sparsity in the feedback path. We demonstrate faster convergence compared with those algorithms, quality improvement of about 0.25 (on a 0–1 objective scale of the hearing-aid speech quality index (HASQI)), and about 5 dB ASG improvement compared with the normalized LMS (NLMS).

This paper proposes a novel adaptive feedback cancellation (AFC) architecture to improve the performance of an existing robust AFC method in the presence of noisy speech conditions. By employing a computationally efficient Spectral flux (SF) feature-based unsupervised voice activity detector (VAD), we adaptively control the step sizes in the proposed AFC algorithm (SFPEM-AFC). The proposed AFC method achieves faster convergence and lower misalignment errors than earlier methods. Objective evaluation of the AFC algorithm is presented using Signal to Feedback Ratio (SFR) and Misalignment (MISA) values for several noisy conditions. The Proposed method shows a significant reduction in the MISA values while maintaining higher SFR and higher perceptual quality over the earlier methods. Experimental results are presented for realistic noisy conditions to demonstrate the superiority of the proposed noise-adaptive AFC method for hearing aid devices (HADs).

In adaptive feedback cancellation systems, the partitioned block frequency-domain adaptive filter (PBFDAF) is commonly adopted to improve the computational efficiency and convergence rate while introduce a small inherent delay. However, the PBFDAF with a fixed step-size will compromise among fast convergence speed and low steady-state misalignment. In this paper, a frequency dependent convex combination of two PBFDAFs operating with different step sizes is applied, in order to achieve both fast convergence speed and low steady-state misalignment. Moreover, we design a weight transfer procedure to further improve the overall convergence performance by transferring a portion of weights of the fast filter to the slow one. Simulations results demonstrate that the proposed algorithm outperforms the affine combinaiton based method.

This paper addresses trade-offs in adaptive feedback cancellation (AFC) for hearing aids. Aggressive AFC for improved added stable gain (ASG) reduces speech quality. In this paper, the hearing-aid speech quality index (HASQI) is used to investigate AFC performance before the system becomes unstable. It is demonstrated that for a desired speech quality, multiple AFC algorithms can be evaluated for their ASG and computational efficiency. An example is presented with HASQI = 0.8, baseline AFC, and two advanced approaches. For the advanced AFCs, ASG gains of 4 and 7 dB were obtained at additional computational complexity of 8% and 11%, respectively.

Acoustic feedback is a notable issue in hearing aid devices, which is produced by the undesired acoustic coupling between the microphone and speaker. Under specific conditions, it creates the howling effect, which is very irritating for the hearing aid user. In this way, the acoustic feedback limits the amplification of hearing aid. The objective of Adaptive Feedback Cancellation (AFC) is to adaptively model the feedback path in such a way that the estimated signal is subtracted from the microphone signal. The main issue in estimating the feedback path model is the correlation between the microphone and speaker signal, which is caused by the closed signal loop. Continuous adaptation feedback cancellation techniques suffer from a modeling error or biasing if the microphone signal is spectrally colored such as speech signals. In this paper, a Prediction Error Method (PEM) based AFC is discussed. The complete system is evaluated extensively for various speech signals for different feedback scenarios with feedback paths measured for real time simulations.

Acoustic feedback cancellation in modern hearing aids is often performed using adaptive filters, in which the convergence rate has typically to be limited in order to maintain satisfactory steady-state performance. This can lead to slow feedback cancellation upon rapid feedback path changes, e.g., when a phone is moved towards the user's ear. In this work, we introduce a novel method by using spectro-temporal modulation in combination with an adaptive filter. We demonstrate through simulation experiments that when the traditional adaptive filter method has an insufficient convergence rate and thereby fails to cancel feedback upon rapid feedback path changes, the proposed system removes feedback immediately.

In real-time applications, such as acoustic feedback cancellation in hearing aids, it is desired that the adaptive algorithm converges fast towards a good estimate of the feedback path, while incurring a low steady-state error. A high adaptation step of the least mean square (LMS) algorithm allows for a faster convergence but compromises the steady-state misalignment of the adaptive filter. In this paper, we propose the use of an affine combination of two adaptive filters with different step sizes, but a common combination parameter to break the precision-versus-speed compromise of the adaptive algorithm. Moreover, an improved version of this affine-combination scheme using a different combination parameter for each filter coefficient is also applied for better tracking. Further, a more sophisticated algorithm, which utilizes a convex combination of adaptive filters in a three-filter scheme along with the varying step size (VSS) approach, is introduced for an improved tracking and faster convergence to eliminate the effect of the feedback path. To reduce the estimation bias due to closed-loop signal correlations, the linear prediction-based adaptive feedback cancellation (AFC) design is used instead of a basic adaptive feedback canceller. Simulation results show that the convex combination schemes provide better feedback-cancellation performance than the single-filter VSS algorithm.

In this paper, a tap-length-adaptation algorithm is proposed for adaptive feedback cancellation (AFC) in the adaptive filter used in digital hearing aids. The existing state-of-the-art feedback cancellers use a fixed-length adaptive filter, which might over-model or under-model the feedback path. To overcome the issue of selecting a fixed arbitrary tap-length value, a unique automatic tap-length-variation algorithm is proposed for the adaptive filter. A key tap-length-update parameter, which ensures a balance between the deviation between steady-state and optimal values of the tap length and influences the convergence speed of the tap-length adaptation, has also been made adaptive for efficient feedback path estimation. A detailed mathematical performance-analysis of the proposed algorithm at steady state is performed. Computer simulations verify that the derived expressions predict the proposed algorithm’s improved performance over that of the existing algorithms.

This paper proposes a novel algorithm and architecture for the adaptive feedback cancellation (AFC) based on the pitch and the formant information for hearing aid (HA) applications. The proposed method, named as Pitch based Formant Estimation (PFE-AFC), has significantly low complexity compared to Prediction Error Method AFC (PEM-AFC). The proposed PFE-AFC consists of a forward and a backward path processing. The forward path processing includes a low complexity pitch based formant estimator for decorrelation filter coefficients update and a pitch based voice activity detector for speech detection, which facilitates the feedback cancellation filter in the backward path to reduce feedback component and maintain speech quality. From system point of view, the PFE-AFC has low complexity overhead since it is easy to share computation resource with other components in the HA system, such as noise reduction and auditory compensation. In addition, the PFE-AFC is suitable for hardware implementation owing to its regular structure. Complexity evaluations show that the PFE-AFC has four orders lower complexity than the PEM-AFC. Simulation results show that the PFE-AFC and the PEM-AFC can achieve similar PESQ (perceptual evaluation speech quality) and ASG (added stable gain). Moreover, the proposed PFE-AFC can outperform the conventional AFC.

Adaptive feedback cancellation methods may integrate the use of probe signals to assist with the biased optimal solution in acoustic systems working in closed-loop. However, injecting a probe noise in the loudspeaker decreases the signal quality perceived by users of assistive listening devices. To counter this, probe signals are usually shaped to provide some level of perceptual masking. In this letter we show the impact of using a shaping filter on the system behavior in terms of convergence rate and steady state error. From this study, it can be concluded that shaping the probe signal may have detrimental influence in terms of system performance. Accordingly, we propose to use the unshaped probe signal combined with an inverse filter of the shaping filter to identify the feedback channel. This restructure of the problem restores convergence rate of LMS type algorithms. Furthermore, we also show that an adequate forward path delay is required to obtain an unbiased solution and that the suggested scheme reduces this delay.

A novel approach is proposed for improving adaptive feedback cancellation using a variable step-size affine projection algorithm (VSS-APA) based on global speech absence probability (GSAP). The variable step-size of the proposed VSS-APA is adjusted according to the GSAP of the current frame. The weight vector of the adaptive filter is updated by the probability of the speech absence. The performance measure of acoustic feedback cancellation is evaluated using normalized misalignment. Experimental results demonstrate that the proposed approach has better performance than the normalized least mean square (NLMS) and the constant step-size affine projection algorithms.