A comparative study between different pre-whitening decorrelation based acoustic feedback cancellers

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.

Adaptive feedback cancellation in hearing aids

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

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.

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.

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.

This paper proposes a perceptual comparative evaluation of some Adaptive Feedback Cancellation (AFC) methods based on decorrelating prefilters. Our objective is to identify suitable AFC methods which give the lowest audible degradation of the amplified speech. Hence, some perceptual criteria measuring only the audible part of the degradation are defined. We show that for hearing impaired subjects who suffer from moderate and severe hearing loss, the perceived distortions and oscillations are lower quantities, compared to the total degradation commonly measured using classical criteria. Moreover, the results of the comparative evaluation of some existing decorrelation methods show that a novel proposed approach based on the reduction of both auto-correlation and intercorrelation between signals (BRP-IVM-AFC) provide the best auditive quality for moderate hearing loss and that the method based on frequency bands reduction (bandlimited-AFC) provides an improvement of the perceived auditive quality for severe hearing loss.

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.

The use of an adaptive feedback canceler (AFC) for howling suppression in hearing aids seems very attractive since it is not only unaffected by the changes in the operating environment, but it also limits signal degradation due to the feedback signal. This, however, requires a reference signal which is correlated with the feedback signal but not with the input signal. In hearing aids, such a signal is hard to obtain. The output signal could be used as reference if its correlation with the input signal could sufficiently be removed. If the reference signal is correlated with the input signal, the input signal will also be canceled by the AFC. Here, the use of a frequency compressor as a decorrelator is proposed. The performance of this system is then investigated via digital simulation. Results indicated that with the use of the proposed system and the proper choice of system parameters, an increase of about 18 dB in the howling margin could be achieved with minimal deterioration in output signal quality.

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.

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.

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.

The topic of compression has been discussed quite extensively in the last 20 years (eg, Braida et al., 1982; Dillon, 1996, 2000; Dreschler, 1992; Hickson, 1994; Kuk, 2000 and 2002; Kuk and Ludvigsen, 1999; Moore, 1990; Van Tasell, 1993; Venema, 2000; Verschuure et al., 1996; Walker and Dillon, 1982). However, the latest comprehensive update by this journal was published in 1996 (Kuk, 1996). Since that time, use of compression hearing aids has increased dramatically, from half of hearing aids dispensed only 5 years ago to four out of five hearing aids dispensed today (Strom, 2002b). Most of today's digital and digitally programmable hearing aids are compression devices (Strom, 2002a). It is probable that within a few years, very few patients will be fit with linear hearing aids. Furthermore, compression has increased in complexity, with greater numbers of parameters under the clinician's control. Ideally, these changes will translate to greater flexibility and precision in fitting and selection. However, they also increase the need for information about the effects of compression amplification on speech perception and speech quality. As evidenced by the large number of sessions at professional conferences on fitting compression hearing aids, clinicians continue to have questions about compression technology and when and how it should be used. How does compression work? Who are the best candidates for this technology? How should adjustable parameters be set to provide optimal speech recognition? What effect will compression have on speech quality? These and other questions continue to drive our interest in this technology. This article reviews the effects of compression on the speech signal and the implications for speech intelligibility, quality, and design of clinical procedures.

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.

An overview of the HASPI and HASQI metrics for predicting speech intelligibility and speech quality for normal hearing, hearing loss, and hearing aids