Adaptive Feedback Cancellation Research Articles

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.

Switched Combination of Simplified Kalman Filter and Affine Projection Algorithm for Acoustic Feedback Cancellation

Acoustic coupling between the microphone and the loudspeaker is a major issue in open-fit digital hearing aids. When compared to a close-fit hearing aid, an open-fit dramatically reduces signal quality and limits the potential maximum stable gain. Adaptive feedback cancellation (AFC) is a practical method for reducing the influence of acoustic coupling. However, because to the high correlation between the loudspeaker signal and the incoming signal, it might induce bias in calculating the feedback path if not carefully considered, especially when the incoming signal is spectrally coloured, as in speech and music. For decreasing this bias, the prediction error method (PEM) is well recognised. In this paper we proposed a switched PEM based hybrid combination of simpliedkalman filter and affine projection algorithms (H-SKF-APA), with soft-clipping that allows for further increase in convergence/tracking rates, resulting in a better ability to recover from an unstable or howling state. Simulation results showed that the proposed algorithm performed better.

Adaptive feedback cancellation based on interaural level difference using lattice filter with correlation control for binaural hearing devices

This paper proposes a correlation control method for linear prediction filters based on howling detection using binaural information in hearing aids. In a closed-loop system, high autocorrelation of the input signal can cause estimation errors owing to biased solutions, resulting in an acoustic artifact known as entrainment. Adaptive feedback cancellation (AFC) based on the linear prediction error method (PEM) has been successfully used as a solution to this bias. However, if the convergence speed of the linear prediction filter is faster than the estimated speed of the feedback path, removal of the feedback signal would be difficult. The proposed method uses a strategy to improve convergence by detecting howling based on the interaural level difference (ILD) and controlling the decorrelation performance of the correlation control algorithm of the adaptive lattice filter. Experimental results indicate that the adaptive filter of the proposed method significantly improves the estimation accuracy and tracking performance. The robustness of the proposed method is also confirmed via objective and subjective evaluation experiments on the generation of artifacts. Experimental results obtained using autocorrelated input signals show that the proposed method significantly improves robustness to entrainment compared to the conventional bilateral AFC method.

Acoustic Feedback Cancellation Algorithm for Hearing Aids Based on a Weighted Error Adaptive Filter

Acoustic feedback is a common phenomenon that occurs during hearing aid use, limiting the maximum gain that a hearing aid can provide. Effective cancellation of acoustic feedback is an essential feature of hearing aids. However, due to the complex environments in which hearing aids are used and the frequently changing acoustic feedback path, it is difficult for existing adaptive filter-based acoustic feedback cancellation algorithms to balance both convergence speed and steady-state error. For this reason, based on the nonparametric variable step size (NPVSS) algorithm, a weighted NPVSS algorithm that also introduces a prediction error method is proposed in this paper. First, by introducing the prediction error method, the adaptive filter bias caused by the nonwhite source signal is effectively reduced. Second, the proposed weighting mechanism weights the error signal according to the adaptive filter misalignment, which enhances the steady-state robustness of the algorithm while accelerating its convergence. In addition, a new low-complexity method is herein proposed for source signal energy estimation by reusing the misalignment information to solve the step size calculation problem of the NPVSS algorithm. Simulation results show that the new algorithm exhibits greater robustness and faster convergence than similar algorithms. The proposed algorithm is implemented with a real hearing aid and its performance is measured on a dummy head in a soundproof room. The test results demonstrate that the proposed algorithm achieves a 35% reduction in convergence time compared with PEM-IMLMS and a 60% reduction compared with PEM-NLMS. Furthermore, the proposed algorithm reduces the sound pressure level of acoustic feedback residues compared with PEM-IMLMS and PEM-NLMS by approximately 2 dB SPL and 6 dB SPL, respectively. These results indicate that the new algorithm can provide timely and stable cancellation of acoustic feedback.

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.

A deep learning solution to the marginal stability problems of acoustic feedback systems for hearing aids

For hearing aids, it is critical to reduce the acoustic coupling between the receiver and microphone to ensure that prescribed gains are below the maximum stable gain, thus preventing acoustic feedback. Methods for doing this include fixed and adaptive feedback cancellation, phase modulation, and gain reduction. However, the behavior of hearing aids in situations where the prescribed gain is only just below the maximum stable gain, called here "marginally stable gain," is not well understood. This paper analyzed marginally stable systems and identified three problems, including increased gain at frequencies with the smallest gain margin, short whistles caused by the limited rate of decay of the output when the input drops, and coloration effects. A deep learning framework, called deep marginal feedback cancellation (DeepMFC), was developed to suppress short whistles, and reduce coloration effects, as well as to limit excess amplification at certain frequencies. To implement DeepMFC, many receiver signals in closed-loop systems and corresponding open-loop systems were simulated, and the receiver signals of the closed-loop and open-loop systems were paired together to obtain parallel signals for training. DeepMFC achieved much better performance than existing feedback control methods using objective and subjective measures.

A Switched Algorithm for Adaptive Feedback Cancellation Using Pre-Filters in Hearing Aids.

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.

Feedback cancellation in digital hearing aids using convex combination of proportionate adaptive algorithms

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 δn 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.

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.

Fast and efficient acoustic feedback cancellation based on low rank approximation

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.

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.

Active Middle Ear Implant Patient Outcomes With Adaptive Feedback Canceller.

Evaluation of the audiological patient performance with an upgrade of the firmware from the fixed feedback canceller (FFC) to the adaptive feedback canceller (AFC) on an active middle ear implant. Retrospective observational nonrandomized group study. Private hospital. From March 2018 to September 2019, 15 patients implanted with an active middle ear implant, with 6 or more months of experience with a FFC system, were upgraded to an AFC algorithm. Functional gain, speech perception in silence and in noise, and sound localization capacities were examined. Feedback reduction was also analyzed. Thirteen patients were analyzed. Pure tone audiometric evaluation with FFC (mean value of 48.02 dB) compared with AFC at 1 (mean value of 49.12 dB) and 6 months (mean value of 42.75 dB) revealed no statistically significant differences (p = 0.889 and p = 0.358 respectively).In speech discrimination in silence, clinically relevant improvements were observed with AFC at 1 and 6 months, with a mean value of 41.5 and 38.3 dB, respectively (p = 0.03 and p = 0.021 correspondingly). In speech discrimination in noisy environments, we observed an improvement of the different conditions tested. No differences were found in localization capacities between FFC and AFC at the two different moments of evaluation. AFC is more effective than FFC in cancelling feedback and improving sound quality, allowing for better speech understanding in silence and in noise.

Design of a class of zero attraction based sparse adaptive feedback cancellers for assistive listening devices

Acoustic feedback is a frequently encountered problem in assistive listening devices (ALDs). Feedback paths in ALDs are typically sparse in nature and sparsity aware adaptive feedback cancellers can improve perceived audio quality under such scenarios. In an endeavour to improve the feedback canceller performance, a decorrelated polynomial zero attraction (DPZA) normalized least mean square (NLMS) feedback canceller is proposed in this paper. DPZA-NLMS algorithm is seen to have higher computational complexity. Hence, in an attempt to reduce computational complexity, a decorrelated l0-NLMS (D-l0-NLMS) and a decorrelated Versoria zero attraction NLMS (DVZA-NLMS) based feedback canceller are also proposed. Feedback canceller performance in terms of convergence and tracking performance as well as speech/audio quality and speech intelligibility is compared. In addition, computational complexity and memory requirements of the algorithms are also compared thus providing a hearing aid designer with better trade off choices between computational requirements and feedback cancellation performance.

Acoustic Feedback Cancellation in Efficient Hearing Aids using Genetic Algorithm

Many people are distracted from the normal life style, because of the hearing loss they have. Most of them do not use the hearing aids due to various discomforts in wearing it. The main and the foremost problem available in it is; the device introduces unpleasant whistling sounds, caused by the changing environmental noise, which is faced by the user daily. This paper describes the development of an algorithm, which focuses on the adaptive feedback cancellation, that improves the listening effort of the user. The genetic algorithm is one of the computational technique, that is used in enhancing the above features. The performance can also be compared with other comprehensive analysis methods, to evaluate its standards.

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.

On mitigating acoustic feedback in hearing aids with frequency warping by all-pass networks

Acoustic feedback due to the acoustic coupling between the microphones and loudspeakers at high gains can cause the hearing aid system to become unstable. This instability results in brief “howling” artifacts as magnitude and phase conditions fulfill the Nyquist stability criterion (NSC). We present a novel approach that uses all-pass networks to perform nonlinear spectral mapping that we call “freping,” a portmanteau for frequency warping. In this contribution, we focus on spectral manipulations on top of adaptive feedback cancellation (AFC) to break NSC for improved feedback reduction. A real-time, multichannel realization is presented, which has individual control of the warping degree in each frequency band. Freping helps mitigate the NSC and thus leads to improved feedback control, while distortions due to freping are fairly benign based on informal subjective assessments. Our current findings indicate that improvements in terms of the perceptual evaluation of speech quality (PESQ) and the hearing-aid speech quality index (HASQI) can be achieved with freping for a basic AFC (PESQ: 2.56 to 3.52 and HASQI: 0.65 to 0.78) at medium amplification; and an advanced AFC (PESQ: 2.75 to 3.17 and HASQI: 0.66 to 0.73) at high amplification. Additional results will be included in the final presentation.

Noise management features of the open speech platform

In this contribution, we present the noise management features of the Open Speech Platform (OSP) for hearing aid (HA) research. OSP includes basic HA modules (i) subband decomposition, (ii) wide dynamic range compression and (iii) adaptive feedback cancellation; a baseline single-channel speech enhancement (SE) based on Wiener filtering for noise subtraction. We extended OSP noise management to include a generalized sidelobe cancellation (GSC) beamforming between left and right channels, followed by the SE. Time is the scarcest resource, followed by central processing unit (CPU) resources in commercial HAs. With the proposed GSC + SE approach, we were both time and CPU limited. Release 2019a of OSP has 5.6 ms end-to-end latency and GSC requires an additional 5 ms, putting us above the 10 ms requirement. The wearable device of OSP has 4 cores, C0 – C3. C0 is used for all non-realtime tasks such as kernel, embedded web server, etc. and remaining cores are used for realtime tasks. Naive realization of GSC results in one or more cores not meeting the realtime constraints, resulting in audible artifacts. We present optimizations to meet time and CPU budgets; and preliminary objective and subjective results of the proposed system.

Automatic tap-length adjustment of adaptive filter for feedback attenuation in hearing aids

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.

On Mitigating Acoustic Feedback in Hearing Aids with Frequency Warping by All-Pass Networks.

Acoustic feedback control continues to be a challenging problem due to the emerging form factors in advanced hearing aids (HAs) and hearables. In this paper, we present a novel use of well-known all-pass filters in a network to perform frequency warping that we call "freping." Freping helps in breaking the Nyquist stability criterion and improves adaptive feedback cancellation (AFC). Based on informal subjective assessments, distortions due to freping are fairly benign. While common objective metrics like the perceptual evaluation of speech quality (PESQ) and the hearing-aid speech quality index (HASQI) may not adequately capture distortions due to freping and acoustic feedback artifacts from a perceptual perspective, they are still instructive in assessing the proposed method. We demonstrate quality improvements with freping for a basic AFC (PESQ: 2.56 to 3.52 and HASQI: 0.65 to 0.78) at a gain setting of 20; and an advanced AFC (PESQ: 2.75 to 3.17 and HASQI: 0.66 to 0.73) for a gain of 30. From our investigations, freping provides larger improvement for basic AFC, but still improves overall system performance for many AFC approaches.

Adaptive feedback cancellation with prediction error method and howling suppression in train public address system

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.