Adaptive Feedback Cancellation Algorithms Research Articles

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.

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.

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.

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.

Efficient Modeling of Acoustic Feedback Path in Hearing Aids by Voice Activity Detector-Supervised Multiple Noise Injections.

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.

Subjective and Objective Sound-Quality Evaluation of Adaptive Feedback Cancellation Algorithms

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.

다채널 디지털 보청기에 적용 가능한 Adaptive Feedback Cancellation 알고리즘 구현

다채널 디지털 보청기에 적용 가능한 Adaptive Feedback Cancellation 알고리즘 구현

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 Semidefinite Programming Approach to Min-max Estimation of the Common Part of Acoustic Feedback Paths in Hearing Aids

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.

Adaptive Feedback Cancellation for Realistic Hearing Aid Applications

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.

Improved prediction error filters for adaptive feedback cancellation in hearing aids

Acoustic feedback is a well-known problem in hearing aids, caused by the undesired acoustic coupling between the hearing aid loudspeaker and microphone. Acoustic feedback produces annoying howling sounds and limits the maximum achievable hearing aid amplification. This paper is focused on adaptive feedback cancellation (AFC) where the goal is to adaptively model the acoustic feedback path and estimate the feedback signal, which is then subtracted from the microphone signal. The main problem in identifying the acoustic feedback path model is the correlation between the near-end signal and the loudspeaker signal caused by the closed signal loop, in particular when the near-end signal is spectrally colored as is the case for a speech signal. This paper adopts a prediction-error method (PEM)-based approach to AFC, which is based on the use of decorrelating prediction error filters (PEFs). We propose a number of improved PEF designs that are inspired by harmonic sinusoidal modeling and pitch prediction of speech signals. The resulting PEM-based AFC algorithms are evaluated in terms of the maximum stable gain (MSG), filter misadjustment, and computational complexity. Simulation results for a hearing aid scenario indicate an improvement up to 5–7dB in MSG and up to 6–8dB in terms of filter misadjustment.

Adaptive feedback cancellation for audio applications

Acoustic feedback occurs in many audio applications involving musical sound signals. However, research efforts in acoustic feedback control have mainly been focused on speech applications. Since sound quality is of prime importance in audio applications, a proactive approach to acoustic feedback control is preferred to avoid ringing, howling, and excessive reverberation. Adaptive feedback cancellation (AFC) using a prediction-error-method (PEM)-based approach is a promising proactive solution, but existing algorithms are again designed for speech applications only. We propose to replace the all-pole near-end speech signal model in the PEM-based approach with a cascade of two near-end signal models: a tonal components model and a noise components model. We derive the identifiability conditions for joint identification of the acoustic feedback path and the cascaded near-end signal models. Depending on the model structure that is used for the near-end tonal components, three different PEM-based AFC algorithms are considered. By applying some relevant model approximations, the computational overhead of the proposed algorithms compared to the normalized least mean squares (NLMS) algorithm can be reduced to 25% of the NLMS complexity. Simulation results for both room acoustic and hearing aid scenarios indicate a significant performance improvement in terms of the misadjustment and the maximum stable gain increase.

Robust compensation with adaptive feedback cancellation in hearing aids

Howling caused by feedback in hearing aids reduces comfort and limits the hearing aid amplification. Recent studies investigated adaptive feedback cancellation systems which track the response of the feedback path and attempt to cancel the feedback signal therefore reducing the howling. Since in practice the feedback signal is never cancelled completely, the closed-loop system can still become unstable. This paper presents a formulation of a robust hearing aid system with guaranteed closed-loop stability which is free from howling. The system incorporates an adaptive feedback cancellation filter integrated with a robust compensation filter. The limit on the tracking error of the feedback cancellation filter is first defined, from which a limit on the gain of the compensation filter which guarantees robust stability is derived. Then, an adaptive feedback cancellation algorithm with improved robustness is proposed. A simulation example of adaptive feedback cancellation in a hearing aid with a time-varying acoustic leak is then presented, showing the benefit of the robust-adaptive algorithm.

Band-limited feedback cancellation with a modified filtered-X LMS algorithm for hearing aids

Commonly used wideband adaptive feedback cancellation techniques do not provide satisfactory performance for reducing feedback oscillation in hearing aids. In this paper, a band-limited adaptive feedback cancellation algorithm using normalized filtered-X LMS techniques is proposed that provides good cancellation efficiency, convergence behavior and better output sound quality for speech signals, when compared to the wideband approach. Convergence analysis and computer simulations illustrate the advantages of the proposed approach.