Feedback Cancellation In Hearing Aids Research Articles

Robust constrained cosine arctangent based adaptive filtering for feedback control in hearing aids

The striking problem of acoustic feedback remains a persistent challenge in hearing aids (HAs), imposing limitations on attainable amplification and significantly deteriorating sound quality, often manifesting as disruptive howling artifacts. With the objective of feedback mitigation, the feedback path is estimated using an adaptive filter. The constrained least mean square (CLMS) technique is commonly employed in adaptive filtering applications where it is necessary to meet specific linear constraints. However, algorithm's robustness is compromised when impulsive or non-Gaussian noise interference occurs. In lieu of this, a novel algorithm known as the robust constrained cosine arctangent (CCAT) adaptive filtering is introduced to enhance the convergence behavior. This has been formulated with the inclusion of linear constraints to the suggested cosine function integrated with the arctangent technique. This algorithm aims to address the issue of feedback cancellation in hearing aids along a set of constraints. To achieve a minimal steady-state error and expedite the convergence of the method, a novel policy that adjusts the step factor has been suggested. CCAT-VSS is a modified version of CCAT that includes a variable step size (VSS). The simulation findings demonstrate that the suggested method exhibits superior performance for misalignment, added stable gain and certain voice quality assessments.

Curvelet based robust improved sine adaptive filter for feedback cancellation in hearing aids

Acoustic feedback is a common and persistent issue in hearing aids, which can lead to limitations in the achievable amplification and significant degradation in sound quality, including howling artefacts. Recently, the maximum versoria criteria (MVC) algorithm has been developed to reduce the acoustic feedback in hearing aids in the presence of impulsive noise. However, the robust technique MVC has not addressed the feedback signal’s sparseness characteristic while developing an adaptive feedback canceller. This paper proposes a robust and sparsity-aware technique called curvelet-improved sine-based adaptive filtering (CISAF) algorithm for adaptive feedback cancellation (AFC) in hearing aids using the prediction error method (PEM) in the case of non-gaussian interference. The step factor is enhanced based on the logarithmic cosh function to speed up the algorithm’s convergence while guaranteeing a low steady-state error. This variable step size (VSS) is included with CISAF as CISAF-VSS technique. Computer simulations show that the suggested method achieves a good convergence rate, maintains high speech quality, keeps a high segmental SNR (segSNR) and short-time objective intelligibility (STOI) at steady-state.

Evaluation of deep marginal feedback cancellation for hearing aids using speech and music.

Speech and music both play fundamental roles in daily life. Speech is important for communication while music is important for relaxation and social interaction. Both speech and music have a large dynamic range. This does not pose problems for listeners with normal hearing. However, for hearing-impaired listeners, elevated hearing thresholds may result in low-level portions of sound being inaudible. Hearing aids with frequency-dependent amplification and amplitude compression can partly compensate for this problem. However, the gain required for low-level portions of sound to compensate for the hearing loss can be larger than the maximum stable gain of a hearing aid, leading to acoustic feedback. Feedback control is used to avoid such instability, but this can lead to artifacts, especially when the gain is only just below the maximum stable gain. We previously proposed a deep-learning method called DeepMFC for controlling feedback and reducing artifacts and showed that when the sound source was speech DeepMFC performed much better than traditional approaches. However, its performance using music as the sound source was not assessed and the way in which it led to improved performance for speech was not determined. The present paper reveals how DeepMFC addresses feedback problems and evaluates DeepMFC using speech and music as sound sources with both objective and subjective measures. DeepMFC achieved good performance for both speech and music when it was trained with matched training materials. When combined with an adaptive feedback canceller it provided over 13 dB of additional stable gain for hearing-impaired listeners.

Jointly Leveraging Decorrelation and Sparsity for Improved Feedback Cancellation in Hearing Aids.

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.

Sparsity aware affine-projection-like filtering integrated with robust set membership and M-estimate approach for acoustic feedback cancellation in hearing aids

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 l1 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.

An Adaptive Time‐Domain Kalman Filtering Approach to Acoustic Feedback Cancellation for Hearing Aids

The adaptive filtering approach has been widely used for acoustic feedback control in the hearing aids due to its excellent performance. The commonly used adaptive filtering algorithms employ a fixed step-size, which has to compromise between the initial convergence and the steady-state misalignment. Many variable stepsize adaptive algorithms have been proposed to handle this problem. In this paper, we propose a broadband Kalman filter to resolve this problem. The acoustic feedback path is modelled by a first-order Markov model, and the observation equation is constructed using more past data vector. A major issue in the hearing aids is the computational complexity. We thus present a simplified version to reduce the complexity, which bridges between the exact Kalman filter and the affine projection algorithm. The estimation of the process and measurement noise variance is discussed in detail. A two-feedback path model is adopted to improve the algorithm's lack of re-convergence. Simulation results confirm the proposed algorithm clearly outperforms the other variable step-size adaptive filtering approaches.

Adaptive Feedback Cancellation in Hearing Aids Based on Orthonormal Basis Functions With Prediction-Error Method Based Prewhitening

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.

Acoustic feedback cancellation in hearing aids using dual adaptive filtering and gain-controlled probe signal

In this paper, we propose a probe signal-based adaptive filtering method for acoustic feedback cancellation (AFC) in hearing aids. The proposed method consists of two adaptive filters. The first adaptive filter is excited by the receiver (loudspeaker) signal, and uses the microphone signal as its desired response. The first adaptive filter shows a fast convergence speed, however, it may converge to a biased solution at the steady-state because its input and desired response are correlated with each other. The second adaptive filter is excited by an internally generated (uncorrelated) probe signal. The two adaptive filters are adapted using a delay-based normalized least mean square (NLMS) algorithm. A strategy is devised to exchange the coefficients of two adaptive filters such that the both adaptive filters give a good (unbiased) estimate of the acoustic feedback path. Furthermore, we propose to vary the gain of the probe signal, such that a high level probe signal is injected during the transient state, and a low level probe signal is used after the AFC system has converged. The computer simulations demonstrate that the proposed method achieves good modeling accuracy, preserves good speech quality, and maintains high output SNR at the steady-state.

Analysing the impact of system dimension on the performance of a variable-tap-length adaptive algorithm

The variable-tap-length algorithms try to find out the optimum number of adaptive filter coefficients that are necessary to model an unknown plant. The most recent variable-tap-length algorithms claim themselves to be independent of the order of the unknown channel, even though the convergence rate of an adaptive finite impulse response filter that is used for identifying an unknown plant in a system identification framework may be affected by the characteristics of the input signal and time-varying filter parameters. The system dimension may vary notably from one application to others, such as acoustic echo cancellation in a big conference hall needs thousands of filter weights, whereas feedback cancellation in hearing-aids requires less than a hundred coefficients. Hence, there is a need to analyze the convergence of the variable-tap-length algorithm for these time-varying applications of adaptive filters of different dimensions. In this paper, we develop and propose a novel technique to measure the convergence and transient behavior of the variable-tap-length adaptive algorithm. Using this analysis, we also arrive at conclusion that the median rate of convergence declines with the rise in system dimension to a limit determined by the reference input. It is proved that the limiting point can be obtained by the level of the autocorrelation of the input signal.

Zero attracting proportionate normalized subband adaptive filtering technique for feedback cancellation in hearing aids

Acoustic feedback cancelers (AFC) employing normalized subband adaptive filtering (NSAF) in conjunction with proportionate gain allocation strategy (PNSAF) have been utilized to mitigate the effect of acoustic feedback in hearing aids. However, it was found to exhibit fast convergence at the initial stage whereas slowing down the convergence at later phase of the adaptation process. In an endeavor to overcome this, l1 norm-based penalty element is incorporated with the PNSAF algorithm proposing a new zero attracting PNSAF (ZA-PNSAF) based AFC system. Further, re-weighted ZA-PNSAF (RZA-PNSAF) algorithm is newly introduced to address the varying sparsity conditions. The efficacy of the proposed method is demonstrated by higher maximum stable gain (MSG) (3–5 dB) and PESQ (perceptual evaluation of speech quality) values obtained over existing algorithms. In addition, the convergence and mean square deviation (MSD) analysis of the proposed algorithm are also carried out. Analysis of the ZA-PNSAF algorithm is validated through comparison of the simulated and analytical MSD values.

Mean square performance evaluation in frequency domain for an improved adaptive feedback cancellation in hearing aids

Abstract 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.

De-Correlated Improved Adaptive Exponential FLAF-Based Nonlinear Adaptive Feedback Cancellation for Hearing Aids

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.

A new evolving-update-based feedback cancellation scheme for hearing aids

This paper proposes an efficient adaptive feedback canceller (AFC) for hearing aid, which provides satisfactory performance both in sparse and in dispersive conditions as well as can adapt according to the variations in the sparseness level of the feedback path for coloured signal as input. This is achieved by incorporating the measure of sparseness intensity and the variable step size to the memory-improved proportionate affine projection algorithm (MIPAPA), and hence, an improved MIPAPA (IMIPAPA) is proposed. Further, in order to reduce the computations incurred by the AFC, an evolving-update IMIPAPA (E-IMIPAPA) is introduced, employing an intermittent update of taps of the adaptive filter by simultaneously adjusting the update interval. The proposed E-IMIPAPA is applied to the two-microphone-based AFC. The results of simulation-based experiments show the effectiveness of the proposed algorithm as compared to the existing methods for feedback cancellation in hearing aid in terms of misalignment and added stable gain. The proposed AFC model is further extended to the multiple-microphone and single-speaker set-up.

Analysis of a prediction error method employing orthonormal basis functions in adaptive feedback cancellation for hearing aids

Hearing aids often suffer from acoustic feedback, which limits the achievable amplification and may severely degrade sound quality by creating howling artifacts. A potential method of feedback cancellation comprises predicting the feedback path (FP) using an adaptive filter. However, a large model error, or bias, is introduced due to signal correlations. To reduce the bias in the FP estimate, a prediction-error-method (PEM) has been used, which is based on a closed-loop identification of the FP and the auto-regressive modeling of the desired signal. This approach, which represents the FP using an FIR filter, requires a large number of parameters. Furthermore, in a reverberant environment, even a high order of the FIR filter may be insufficient to fully represent the FP, which reduces the convergence rate and limits the maximum stable gain of the system. In this contribution, we introduce a PEM-based method that utilizes orthonormal basis functions to precisely predict the acoustic FP in hearing aids. Simulation results with measured data show that the proposed method outperforms the standard PEM adaptation algorithm in terms of convergence rate and maximum stable gain.

An improved CLMS algorithm for feedback cancellation in hearing aids

In LMS algorithm-based feedback estimation, the value of the adaptation step size chosen imposes establishes a compromise between the speed at which the algorithm converges to the feedback-path estimate and the misadjustment between the true and estimated feedback paths at steady state. The combined LMS (CLMS) scheme overcomes this issue, but itself suffers from a sluggish adaptation of the mixture parameter during periods of a rapidly-varying or a stationary feedback path, leading to a degradation in the performance of the feedback canceller. In this work, we propose an acoustic feedback canceller with an improved affine combination of two different-step-size LMS filters, for a bias-less estimation of the acoustic feedback. The new filter-combiner parameter controls the filter combination and ensures at least a minimum adaptation of the mixture parameter for a stationary as well as a varying acoustic environment. We analyse the proposed algorithm for feedback reduction and prove that it performs as well as the element filters or even better in some situations, as compared to the CLMS algorithm. A detailed behaviour analysis of the proposed algorithm is also presented for scenarios of a stationary as well as a time-varying acoustic environment of the user. Simulation results verify the validity of the derived expressions.

Convex combinations of adaptive filters for feedback cancellation in hearing aids

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.

Individual-activation-factor based novel approach for acoustic feedback suppression in hearing aid

The conventional adaptive filtering algorithms do not perform satisfactorily while modeling sparse impulse response of the feedback path of hearing aid in the presence of real input signals which are colored in nature. Furthermore, the high computational overhead of these adaptive feedback cancelers (AFC), make the situation difficult for a real-time, power and area constrained device like hearing aid. In an endeavor, to overcome these limitations, a de-correlated memory proportionate affine projection-like algorithm (DMPAPL) with individual-activation-factor (IAF-DMPAPL) is proposed for improved acoustic feedback cancellation in hearing aids. The concept of individual activation factor results in better distribution of the adaptation energy over the adaptive filter taps while estimating sparse impulse response. An adaptive de-correlation filter along with memory of previous proportionate gain factor employed to affine-projection-like algorithm offers improved modeling accuracy along with reduced computational cost. The corresponding update rule and bound on the learning rate of the proposed feedback cancellation system has been derived. Extensive simulation study demonstrates the efficacy of the proposed AFC over other methods for various real input signals in case of different feedback path scenarios.

Correlation Detection for Adaptive Feedback Cancellation in Hearing Aids

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.

A robust adaptive hybrid feedback cancellation scheme for hearing aids in the presence of outliers

It has been demonstrated that the Filtered-x Wilcoxon LMS (FxWLMS) based adaptive filter mitigates the effect of the outliers acquired by the microphone signal of hearing aids by minimizing the Wilcoxon norm and hence shows better cancellation performance than the existing Filtered-x LMS (FxLMS) algorithm. The prediction error method based adaptive feedback canceller (PEMAFC) reduces the bias present in the estimate of the feedback path due to the continuous adaptive filtering (CAF). However, the impulse response of the measured feedback path is close to zero for the first many samples due to the delay introduced by ADC converters and then contains few significant values, which results in slow convergence rate when an adaptive filter is used to model the same. To overcome this limitation, we propose a proportionate normalized WLMS (PNWLMS) algorithm based PEMAFC (P-PNWLMS) for feedback cancellation in hearing aid in the presence of outliers. Further, with an objective to improve the convergence rate and performance accuracy simultaneously, this paper proposes a novel convex PNWLMS (CPNWLMS) algorithm which incorporates convex combination of PNWLMS and WLMS algorithms. The weight update equations are derived for PEMAFC trained by PNWLMS (P-PNWLMS) and CPNWLMS (P-CPNWLMS) algorithms respectively. The results of the simulation study show improved performance of the proposed CPNWLMS based adaptive filter over its component filters.

An improved block adaptive system for effective feedback cancellation in hearing aids

The hearing aid being a battery operated, portable device requires short processing delay, low computational complexity, with appreciable acoustic feedback cancellation effect. The prediction error method (PEM) and PEM with shadow filter (PEM-SH) based adaptive feedback canceller (AFC) referred as PEMAFC and PEMAFC-SH respectively reduces the amount of bias present in the estimate of feedback path. The available partitioned block frequency domain adaptive filter (PBFAF) based implementation of PEMAFC (PBFAF-P) and PEMAFC-SH (PBFAF-PS), offers a potential option for modelling an adaptive filter with many taps along with short block processing delay. However, the PBFAF suffers from large computational load because of the involvement of computationally expensive gradient constraints in each partition. Though removing or alternately applying the gradient constraint saves some computations but it results in significant performance degradation. With an objective of substantially reducing the computational burden and simultaneously retaining the performance, this paper develops an improved partitioned block Hartley domain adaptive filter (IPBHAF) and then employs it for effective feedback cancellation in hearing aids. Further, the IPBHAF with modified step size (IPBHAF-M) is proposed to achieve both fast convergence and better steady state performance. The simulation based experiments demonstrate the superior performance of IPBHAF-M based implementations of PEMAFC (IPBHAF-MP) and PEMAFC-SH (IPBHAF-MPS) over the PBFAF-P and PBFAF-PS in terms of both computational complexity and feedback cancellation performance.