Adaptive feedback cancellation with frequency compression for 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.

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 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

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.

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.

A input signal for a hearing device signal processing unit is generated with the aid of an electro-acoustic converter. An output signal is generated by applying signal amplification. The signal amplification is adapted by determining a reference level of the input signal using statistical calculation methods. A minimum input signal level is determined by subtracting a predefined difference value from the reference level of the input signal. An amplification function is applied to the input signal when the signal level is less than the minimum input signal level. This amplification function is different from an amplification function applied when the signal level is greater than the minimum input signal level.

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.

The use of an adaptive feedback canceller (AFC) based on signals pre-whitening/filtering in hearing aids seems very attractive, since it limits desired signal degradation when amplification gain is increased. In this paper, we present a comparative assessment of the performances of some existing pre-whitening decorrelation based methods. The used criteria consider the adaptive filter performances, the system stability and the speech quality in terms of distortion and oscillation. Results show that the method including the loudspeaker pre-whitening and the microphone filtering is the best. Moreover, the use of an adequate algorithm for adaptive pre-whitener based on the minimization of a criterion which considers the inter-correlation between the pre-whitened loudspeaker and the filtered microphone signals improves more the performances.

Current standardized methods for the measurement of hearing‐aid performance (Specification of Hearing Aid Characteristics, S3.22‐1976, American National Standards Institute, New York) call for the measurement of two special characteristics of compression hearing aids, namely the dynamic and input‐output characteristics, at 2 kHz. A more thorough characterization of compression hearing aids requires measurements at more than one volume‐control setting to determine if the compression is controlled by the input or output signal. Also useful are random‐noise transfer functions and transmissibility measurements to determine the response to multifrequency signals, input‐output characteristics at several frequencies to determine the compression behavior in various frequency bands, and correlation coefficients to examine the influence of nonlinearities and internal noise in the hearing aid upon its performance. Measurements of these parameters are shown at two volume‐control settings for several commercially available hearing aids and their general applicability is discussed. [Work supported by the Veterans Administration.]

This article explores the feasibility of digital bandpass filters within the LabVIEW environment. To facilitate the study of digital filters using virtual instruments, a dedicated digital filter simulation program was developed. This program provides users with various test mode views to aid in examining the Butterworth digital filter and modifying input actions and parameters. These parameters include throughput, order, rank, and slope; amplitude, offset, and frequency; the addition of noise types, such as white noise, over the input signal; selection of digital filter types, specifically bandpass filters; waveforms; graphical representations of input and output signals; and graphical representations of the harmonic spectrum in the filtered signal.

The problem of creating mathematical models of nonlinear dynamical systems does not have an unambiguous solution and requires the creation of a separate synthesis method for each such object. To develop a method for synthesizing mathematical models of an extensive class of nonlinear dynamical systems with polynomial nonlinearities. The work uses a method based on the solution of the Volterra integral equation in the ideology set forth in Van Trees H.L., according to which the structure of a nonlinear dynamical object present47s a series connection of the linear part, characterizing the inertial properties of the system, and the nonlinear element, given by static characteristic. The difference of the suggested version of the method from the classical one, proposed in the works of Van Trees H.L., is an expansion of their input and output signals into Fourier series and a representation of the inertial part of these systems by their Bode plots, connected into one structure with input and output signals and non-linearity by Volterra integral equation. The algorithm of the proposed method is disclosed by the example of solving the problem of identifying a nonlinear dynamical system which impulse response of the inertial part satisfies the separability requirement, the order of the polynomial nonlinearity is three, and the model of the input signal has the form of a sinusoid "raised" over the time axis on a priori given constant level. A computational experiment was carried out on the example of nonlinear dynamical systems with the third order of the nonlinear characteristic and the first and second orders of the model of the inertial part of these systems with the specified algorithms of their parametric identification. The suggested method allows to synthesis the mathematical model of a nonlinear dynamical system with the polynomial static characteristic to the case when the input signal has an arbitrary number of harmonics, and the model of the inertial part and the nonlinear polynomial function have an arbitrary order.

Coherence is a frequency-domain measure of linear dependence between input and output of a system, e.g. a hearing aid, and describes the cumulative effect of different forms of signal corruption, e.g. noise and non-linear distortion. From the coherence function, a general frequency-dependent signal-to-noise ratio can be derived. In this investigation, the applicability of this measuring technique is demonstrated in connection with non-linear distortion in hearing aids. The influence of hearing aid gain and automatic gain control is illustrated, with speech-shaped noise as input signal. For the three hearing aids tested. The gain setting influences the signal-to-noise ratio heavily due to non-linear distortion, especially near maximum gain. The introduction of automatic gain control reduces the effect of non-linear distortion somewhat at high gain settings.