Acoustic Feedback Cancellation in Hearing Aids Exploiting an Inertial Sensor

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 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.

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

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.

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 narrow-band input. In this paper, two procedures for LMS adaptation with a constraint on the magnitude of the adaptive weight vector are derived. The constraints greatly reduce the probability that the adaptive filter will cancel a narrow-band input. Simulation results are used to demonstrate the efficacy of the constrained adaptation.

Adaptive feedback cancellation in hearing aids

A new subband feedback cancellation scheme is proposed, capable of providing additional stable gain without introducing audible artifacts. The subband feedback cancellation scheme employs a cascade of two narrow-band filters Ai(Z)and Bi(Z)ng with a fixed delay, instead of a single filter Wi(Z)and a delay to represent the feedback path in each subband. The first filter, Ai(Z), is called the training filter, and models the static portion of the feedback path in ith subband, including microphone, receiver, ear canal resonance, and other relatively static parameters. The training filter can be implemented as a FIR filter or as an IIR filter. The second filter, B?I?(Z), is called a tracking filter and is typically implemented as a FIR filter with fewer taps than the training filter. This second filter tracks the variations of the feedback path in the i?th? subband caused by jaw movement or objects close to the ears of the user.

Approximated affine projection algorithm for feedback cancellation in hearing aids

The feedback cancellation scheme in hearing aids mainly employs Filtered-XLMS (FXLMS) algorithm However, the conventional FXLMS algorithm doesnot provide satisfactory performance in the presence of outliers acquired by the microphone. In this paper, Filtered-X Wilcoxon LMS (FXWLMS) and Filtered-X least mean log square (FXLMLS) are being introduced as two robust learning algorithms for effective feedback cancellation in the presence of outliers. The simulation studies demonstrate the superior feedback cancellation performance of FXLMLS and FXWLMS over FXLMS in the presence of outliers.

The adaptive feedback canceller (AFC) based on the least mean square (LMS) or normalized LMS (NLMS) algorithm shows poor convergence for sparse impulse response, since they do not consider the characteristics of the impulse response. The acoustic feedback path of the hearing aid exhibits sparse characteristics consisting of few active coefficients and many non active (near to zero) coefficient values. This paper proposes an improved gradient controlled improved proportionate NLMS (IGC-IPNLMS) algorithm for cancelling the feedback phenomenon in hearing aids. The IGC-IPNLMS algorithm employs a variable convergence factor, an estimate of the gradient vector for allocating step size, the controlling parameter of the IGC-IPNLMS algorithm is also updated in each iteration based on the sparseness measure. Further, the reduction in computational complexity is achieved by using sparse partial update method along with the IGC-IPNLMS algorithm (SIGC-IPNLMS) for feedback cancellation. The simulation results show the superior performance of the proposed method for white noise and speech segment as input.

Howling is very annoying problem to the hearing-aid users and it limits the maximum usable gain of hearing-aid devices. In this paper, we present an efficient feedback cancellation system where a time-varying all-pass filter together with a delay in the forward path is used for decorrelating the input and output signals of the hearing aid plant. The adaptive filter in the hearing aid performs continuous adaptation based on the input signal. The output signal of the hearing aid is processed by a 2nd-order APF whose frequency is varied using a low-frequency modulator. Simulation results show that the proposed algorithm can significantly reduce the weight-vector misalignment with a small delay in the forward path.

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

While several proactive acoustic feedback (Larsen-effect) cancellation schemes have been presented for speech applications with short acoustic feedback paths as encountered in hearing aids, these schemes fail with the long impulse responses inherent to, for instance, public address systems. We derive a new prediction error method (PEM)-based scheme (referred to as PEM-AFROW) which identifies both the acoustic feedback path and the nonstationary speech source model. A cascade of a short- and a long-term predictor removes the coloring and periodicity in voiced speech segments, which account for the unwanted correlation between the loudspeaker signal and the speech source signal. The predictors calculate row operations which are applied to prewhiten the speech source signal, resulting in a least squares system that is solved recursively by means of normalized least mean square or recursive least squares algorithms. Simulations show that this approach is indeed superior to earlier approaches whenever long acoustic channels are dealt with

Low-rank enhanced Hammerstein-spline adaptive filter for sparsity-aware nonlinear 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.