Acoustic feedback cancellation for long acoustic paths using a nonstationary source model

Publication in the conference proceedings of EUSIPCO, Antalya, Turkey, 2005

Acoustic feedback in hearing aids occurs due to the coupling between the hearing aid loudspeaker and microphones. In order to reduce acoustic feedback, adaptive filters are often used to estimate the feedback path. To increase the convergence speed and decrease the computational complexity of the adaptive algorithms, it has been proposed to split the acoustic feedback path into a time-invariant fixed part and a time-varying variable part. A key question of this approach is how to determine the fixed part. In this paper, two approaches are investigated: (1) a digital filter design approach that makes use of the signals of at least two hearing aid microphones and (2) a defined physical location approach using an electro-acoustic model and the signals of one hearing aid microphone and an additional ear canal microphone. An experimental comparison using measured acoustic feedback paths showed that both approaches enable one to reduce the number of variable part coefficients. It is shown that individualization of the fixed part increases the performance. Furthermore, the two approaches offer solutions for different requirements on the effort to a specific hearing aid design on the one hand and the effort during the hearing aid fitting on the other hand.

Acoustic feedback arises from the leakage of sound from the loudspeaker back to the microphone and is a common problem in hearing aids and public address systems. One popular solution to this problem is adaptive feedback cancellation (AFC) which utilizes an adaptive filter (AF) to estimate and subsequently subtract the feedback component from the microphone. Due to the closed-loop system the loudspeaker signal and the incoming signal exhibit a high correlation. However, model-based and more recently learning-based AF methods typically neglect this correlation in their derivation, leading to sub-optimal performance in closed-loop scenarios. In this paper, we propose Neural-AFC, a recurrent neural network (RNN) designed for AFC step-size control optimization, that addresses this problem by including not only the adaptive filter but also the acoustic feedback path and the system gain in the recurrence model. Our experiment results show that when trained within this closed-loop model, Neural-AFC improves the steady-state performance and (re)convergence rate compared to conventional and open-loop RNN-based AF in both hearing aid and public address system applications.

Feedback is a problem in hearing aids which will cause signal degradation and reduce the maximum applicable gain. More specifically, the advantages of open fittings (e.g., minimizing the occlusion effect) are limited by acoustic feedback. Feedback cancelation algorithms are used to overcome these limitations. For the development of such algorithms, the acoustic feedback path of the hearing aid must be known. The acoustic feedback path is not only affected by the outer sound field but by the individual anatomy and physiology as well. In order to quantify these different influences, feedback path measurements were performed on 20 human subjects. The measurements included different static conditions as well as dynamic ones (i.e., repetitive movements were performed during the measurement). Since the sound pressure level must be limited in measurements on human subjects, a valid identification of the feedback path is difficult in many cases, due to a low signal to noise ratio. Therefore, all measurements were performed reciprocally in addition to the direct measurements. Results show that yaw movements only have a small influence on the acoustic feedback path, whereas changes of the outer sound field can have a substantial impact on the feedback path.

Adaptive feedback cancellation in hearing aids

The computational complexity and convergence speed of adaptive feedback cancellation algorithms depend on the number of adaptive parameters used to model the acoustic feedback path. To reduce the number of adaptive parameters it has been proposed to decompose the acoustic feedback path as the convolution of a (time-invariant) common part and a (time-varying) variable part. Typically the problem of estimating all the required coefficients has been formulated as a least-squares optimization problem. In contrast, in this paper we propose to formulate the estimation problem as a minmax optimization problem and show how this is associated with the maximum stable gain of a hearing aid. Experimental results using measured acoustic feedback paths from a two-microphone behind-the-ear hearing aid show that the proposed minmax optimization outperforms the least-squares optimization in terms of maximum stable gain. Furthermore, the robustness of proposed common part decomposition for different feedback paths is evaluated.

In this paper, we propose a novel solution for continuous acoustic feedback neutralization in the digital hearing aids. The proposed method is based on two adaptive filters working in tandem. The error signal of the first adaptive filter is used as a desired response for the second adaptive filter being excited by a low-level (constant) probe signal. Initially at the start up, the first adaptive filter gives a fast convergence, however, its input and desired response are correlated with each other and it may converge to a biased solution at the steady-state. On the other hand, the second adaptive filter, though converging slowly being excited by a low-level probe signal, would give a good steady-state estimate of the acoustic feedback path. An appropriate delay is inserted at the output of the hearing aid, and based-on this delay-based processing, a novel strategy is proposed to exchange the weights between the two adaptive filters. The proposed strategy is such that the misconvergence of the first adaptive filter is avoided, as well as the initial convergence of the second adaptive filter is improved. Extensive computer simulations are carried out which demonstrate the effectiveness of the proposed method.

In adaptive feedback cancellation the computational complexity and the convergence speed are determined by the number of adaptive parameters used to model the acoustic feedback path. Therefore it has been proposed to reduce the number of adaptive parameters by modeling the feedback path as the convolution of a time-invariant common part and a time-varying variable part. While previous approaches have modeled the common part either using only poles or using only zeros, in this paper we propose to use a common pole-zero model and present an iterative method to compute the common poles and zeros. Using measured acoustic feedback paths from a two-microphone behind-the-ear hearing aid it is shown that the proposed model enables either to increase the modeling accuracy given a fixed number of parameters of the variable part or to reduce the number of parameters of the variable part given a desired accuracy.

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

In this paper we propose a new variable step-size of the Least-Mean-Square (LMS) algorithm for reducing acoustic feedback in hearing aids. The new variable step-size is designed to approximate the optimum variable step-size that allows the LMS algorithm to decrease its mean-squared error with the fastest rate. The performance of the proposed variable step-size on the acoustic feedback cancellation is compared via computer simulation with those of three other step-size adjustment methods. The comparisons indicate that the new step-size adjustment method has better dynamic and steady state performances than the other methods. In addition, the computation complexity of the new method is relatively low compared with those of the other methods, rendering it more suitable for solving acoustic feedback in hearing aids.

A probe noise signal can be used in an acoustic feedback cancellation system to prevent biased adaptive estimation of acoustic feedback paths. However, practical experiences and simulation results indicate that whenever a low-level and inaudible probe noise signal is used, the convergence rate of the adaptive estimation is significantly decreased when keeping the steady-state error unchanged. The goal of this work is to derive analytic expressions for the system behavior such as convergence rate and steady-state error for a multiple-microphone and single-loudspeaker audio system, where the acoustic feedback cancellation is carried out using a probe noise signal. The derived results show how different system parameters and signal properties affect the cancellation performance, and the results explain theoretically the decreased convergence rate. Understanding this is important for making further improvements in the existing probe noise approach.

In adaptive feedback cancellation the convergence speed and the computational complexity depend on the number of adaptive param-eters used to model the acoustic feedback path. To improve the convergence speed and reduce the computational complexity, it has been proposed to model the acoustic feedback path as the convolution of a time-invariant common pole-zero part and a time-varying variable part. Previous approaches to estimate all the coefficients minimized the so-called equation-error which possibly suffers from poor estimation accuracy in the vicinity of prominent spectral regions, e.g., spectral peaks. In this paper we therefore propose to minimize the so-called output-error by using a Steiglitz-McBride-like iteration scheme. To ensure the stability of the estimated pole-zero filter a frequency domain constraint is used leading to a quadratic programming problem. Experimental results using measured impulse responses from a two-microphone behind-the-ear hearing aid show that the proposed estimation scheme outperforms the existing estimation scheme in terms of modeling accuracy.

Affine projection algorithms (APA) have been widely employed for acoustic echo cancellation (AEC) since they provide a natural trade-off between convergence speed and computational complexity. However, their application in Acoustic Feedback Cancellation (AFC) in hearing aids so far has been limited due to lack of performance improvement in one microphone settings. A two microphone technique was recently proposed to provide an improved cancellation for a larger class of input signals. This paper proposes to use APA in the two microphone closed-loop feedback cancellation. It is shown that APA has significantly improved the misalignment and maximum stable gain of the system. Moreover, a new variable Gaussian step-size control (VGSS) for APA is also proposed. The simulation results indicate an improvement in convergence speed of the proposed algorithms as compared to previously suggested methods.

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.

In feedback cancellation in hearing aids, an adaptive filter is used to model the feedback path. The output of the adaptive filter is subtracted from the microphone signal to cancel the acoustic and mechanical feedback picked up by the microphone, thus allowing more gain in the hearing aid. In general, the feedback cancellation filter adapts on the hearing-aid input signal, and signal cancellation and coloration artifacts can occur for a narrowband input. The goal in feedback cancellation is to design an algorithm that is computationally efficient, provides 10 dB or more of additional amplifier headroom, and is free from processing artifacts.This chapter begins with a steady-state analysis of the effects of acoustic feedback on the hearing-aid response. This analysis illustrates the factors that can affect the accuracy of the adaptive filter and the filter convergence for systems adapting with or without a probe signal. The characteristics of the feedback path being modeled are then described. An additional concern in designing signal processing for a hearing aid are the processing constraints imposed by a low-power portable device, and these concerns are discussed next. Two forms of constrained adaptation are then derived, and simulation results are used to give a comparison of the constrained adaptation with the conventional unconstrained approach for a system adapting without a probe signal. Further improvements in feedback cancellation performance, particularly a reduction in audible processing artifacts, can be achieved by using the filtered-X algorithm, and this approach is described next.The performance of feedback cancellation is ultimately limited by the characteristics of the room in which the listener is located since the room reflections form part of the feedback path. The chapter concludes with a study of the feedback path in a room along with the feedback cancellation performance for an 8-, 16-, or 32-tap adaptive FIR filter in series with a five-pole nonadaptive filter, and the performance limitations imposed by the room reverberation are discussed.KeywordsImpulse ResponseFeedback PathAdaptive FeedbackAcoustic FeedbackRoom ReverberationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.