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Acoustic feedback due to the acoustic coupling between the microphones and loudspeakers at high gains can cause the hearing aid system to become unstable. This instability results in brief “howling” artifacts as magnitude and phase conditions fulfill the Nyquist stability criterion (NSC). We present a novel approach that uses all-pass networks to perform nonlinear spectral mapping that we call “freping,” a portmanteau for frequency warping. In this contribution, we focus on spectral manipulations on top of adaptive feedback cancellation (AFC) to break NSC for improved feedback reduction. A real-time, multichannel realization is presented, which has individual control of the warping degree in each frequency band. Freping helps mitigate the NSC and thus leads to improved feedback control, while distortions due to freping are fairly benign based on informal subjective assessments. Our current findings indicate that improvements in terms of the perceptual evaluation of speech quality (PESQ) and the hearing-aid speech quality index (HASQI) can be achieved with freping for a basic AFC (PESQ: 2.56 to 3.52 and HASQI: 0.65 to 0.78) at medium amplification; and an advanced AFC (PESQ: 2.75 to 3.17 and HASQI: 0.66 to 0.73) at high amplification. Additional results will be included in the final presentation.Acoustic feedback due to the acoustic coupling between the microphones and loudspeakers at high gains can cause the hearing aid system to become unstable. This instability results in brief “howling” artifacts as magnitude and phase conditions fulfill the Nyquist stability criterion (NSC). We present a novel approach that uses all-pass networks to perform nonlinear spectral mapping that we call “freping,” a portmanteau for frequency warping. In this contribution, we focus on spectral manipulations on top of adaptive feedback cancellation (AFC) to break NSC for improved feedback reduction. A real-time, multichannel realization is presented, which has individual control of the warping degree in each frequency band. Freping helps mitigate the NSC and thus leads to improved feedback control, while distortions due to freping are fairly benign based on informal subjective assessments. Our current findings indicate that improvements in terms of the perceptual evaluation of speech quality (PESQ) and the hearing-ai...

Acoustic feedback is perceived as whistling or howling, which is a major complaint of hearing-aids users. Acoustic feedback cancellation is important in hearing-aids because acoustic feedback degrades performance of the hearing aid device by reducing maximum insertion gain. Adaptive systems for estimate acoustic feedback path and feedback suppression algorithms have been proposed in order to solve this problem. A typical feedback cancellation algorithm is LMS(least mean squares) because of its computational efficiency. However it has problem of convergence performance in high correlated input signal. In this paper, we propose a new variable step-size normalized LMS(least mean squares) algorithm using VAD(voice activity detection) to overcome the limitation of the LMS algorithm. The VAD algorithm is GSAP(global speech absence probability) and the feedback cancellation algorithm is normalized LMS. The proposed algorithm applies different step-size between voice and non-voice using VAD, for high stability, fast convergence speed and low misalignment when correlated inputs, such as speech. The result of simulation with white noise mixed speech signal, the proposed algorithm shows high performance then traditional algorithm in terms of stability, convergence speed and misalignment.

The band-limited linear predictive coding (BLPC) vocoder-based adaptive feedback cancellation (AFC)removes the high-frequency bias, while the low frequency bias persists between the desired input signaland the loudspeaker signal in the estimate of the feedback path. In this paper, we present a BLPC vocoderbasedadaptive feedback canceller with probe noise with an objective of reducing the low-frequency biasin digital hearing-aids. A step-wise mathematical analysis of the proposed feedback canceller is presentedemploying the recursive least square and normalized least mean square adaptive algorithms. Itis observed that the optimal solution of the feedback path is unbiased for an unshaped probe noise,but is biased for a shaped probe signal; the bias term does not consist of correlation between the desiredinput and the loudspeaker output. The identifiability conditions are analysed and it is shown that a delay,greater than or equal to the length of the adaptive filter, must be introduced in the forward path toachieve an unbiased feedback path estimate. Algorithm analysis and computer simulations presentedin this paper justify the reason for selecting the proposed design over the existing BLPC vocoder-basedfeedback cancellation algorithm.

Variable step-size (VSS) schemes are popular to use in acoustic echo cancellation (AEC) contexts. However, their effective implementation in adaptive feedback cancellation (AFC) for hearing aids is still challenging due to the correlation between microphone and loudspeaker signals. We propose an improved practical VSS algorithm (IPVSS) which uses a variable step-size with upper and lower limits to control the update equation of an adaptive filter. The proposed algorithm is implemented for feedback cancellation using the prediction error method. As a result, the overall system has a fast convergence rate, a high tracking rate and a low steady-state error. The performance of the proposed approach has been evaluated for both speech and music incoming signals. The simulation results show that the proposed approach outperforms a system which only utilizes either the lower or the upper fixed step-size used in the IPVSS.

With the aging of the population increase, the need of hearing aids is increasing, along with the wide application of digital signal processing technology, applied to adaptive echo cancellation algorithms in digital hearing aid has been significantly improved and developed. The article will to tell echo cancelation principle, to introduce several common algorithms, such as RLS algorithm, LMS algorithm and NLMS algorithm, and discuss their advantages and disadvantages, puts forward a kind of algorithm which is a combination of NLMS algorithm, and BLMS algorithm, named NBLMS_M-K. Finally, discuss the limitations of the adaptive echo cancellation algorithms in digital hearing aid and the future development of algorithm.

Frequency shifting has been used for acoustic feedback control in audio reinforcement systems since the 1950s. It can be used as a stand-alone system and/or it can be combined with an acoustic feedback cancellation system using adaptive filters. A spectral shifting of the loudspeaker signal in an audio system breaks the acoustic feedback loop, and it decorrelates the reference and error signals to the adaptive filter in the cancellation system, which is useful for getting an unbiased adaptive filter estimation. In this work, we analyze the decorrelation effect from the frequency shifting in an acoustic feedback cancellation system. We show that the influence from the frequency shifting, on the correlation function between the reference and error signals, can be divided into two parts: a fast time-varying part and a slowly time-varying part. Especially the slowly time-varying part of the correlation function leads to a periodically time-varying bias in the adaptive filter estimation, which limits the feedback cancellation performance. We propose a solution to obtain an unbiased estimation by removing the slowly time-varying part in the adaptive filter estimation and we verify it by simulations.

Adaptive feedback cancellers in hearing aids can produce unpleasant sounding distortion artifacts (entrainment) in response to periodic inputs, including music. Reliable objective metrics that predict user-perceived distortion could significantly reduce development costs for new hearing aids. The aim of this study was to gain insight into the ability of different objective metrics to predict subjective ratings of the sound quality of music processed by adaptive feedback cancellation. The metrics tested consisted of perceptual measures from established audio quality models (including Perceptual Evaluation of Audio Quality (PEAQ), PEMO-Q and .Rnonlin). Neural networks were used to map between the values of the perceptual measures and a subjective scale of perceived quality. Training data consisted of values of perceptual measures obtained from ten different excerpts of orchestral music processed by a simplified model of a hearing aid with an adaptive feedback canceller, and corresponding subjective quality ratings from 27 normal hearing subjects. An optimal combination of perceptual measures to use as inputs to a network input was found using an extended Fourier amplitude sensitivity test (EFAST). Our results suggest that the most salient inputs to a multivariate model of measured quality ratings consist of perceptual measures related to spectral noise loudness, modulation differences between clean and processed signals, and correlation-based measurement of nonlinear distortion. The intraclass correlation between mean subjective ratings and the output of a network combining these perceptual measures was high , which compares favorably to results from previous studies of perceptual quality metrics applied to audio signals with other forms of noise or distortion.

Acoustic feedback is one of the main problems in modern hearing aids. It distorts the desired signal and limits the maximum stable gain. Results on acoustic feedback cancellation systems based on the prediction error method of closed-loop identification indicate that they perform better than most alternative solutions. Most available analyses of such systems, however, are limited to steady-state results. This paper presents a transient mean-square analysis of a recently proposed system. The structure is analyzed for slow adaptation and for autoregressive input signals. Analytical models are derived for the mean and mean-square adaptive weight behaviors. This includes a model for the transient behavior of the bias in the feedback path estimator. Monte Carlo simulations are presented to verify the accuracy of the derived models.

The acoustic feedback problem has intrigued researchers over the past five decades, and a multitude of solutions has been proposed. In this survey paper, we aim to provide an overview of the state of the art in acoustic feedback control, to report results of a comparative evaluation with a selection of existing methods, and to cast a glance at the challenges for future research.

We propose a novel low latency smartphone-based application that demonstrates the real-time operation to cancel the negative effects of acoustic feedback arising from the coupling between the speaker and the microphone of the smartphone or similar device utilizing the robust Noise Injection (NI) method. We make use of multiple noise injections of short durations to estimate the filter coefficients of an appropriate order between the speaker and the microphone, in order to perform the feedback cancellation effectively in real-time. Our motive behind the development of this application is to perform an effective acoustic feedback cancellation irrespective of the position of speaker and the microphone on the platform under consideration. With the proposed application, we can estimate the transfer function between speaker and microphone in the changing room acoustics making the feedback cancellation very effective. Objective and subjective tests were conducted and results of the proposed real-time application indicate significant acoustic feedback suppression in the presence of varying environmental conditions.

An active noise control (ANC) system is model dependent/independent if its controller transfer function is dependent/independent on initial estimates of path models in a sound field. Since parameters of path models in a sound field will change when boundary conditions of the sound field change, model-independent ANC systems (MIANC) are able to tolerate variations of boundary conditions in sound fields and more reliable than model-dependent counterparts. A possible way to implement MIANC systems is online path modeling. Many such systems require invasive probing signals (persistent excitations) to obtain accurate estimates of path models. In this study, a noninvasive MIANC system is proposed. It uses online path estimates to cancel feedback, recover reference signal, and optimize a stable controller in the minimum H2 norm sense, without any forms of persistent excitations. Theoretical analysis and experimental results are presented to demonstrate the stable control performance of the proposed system.

A new method for acoustic feedback control is derived, combining an adaptive filter with a decorrelation strategy based on the prediction-error method (PEM) entirely in the frequency domain. This all-frequency-domain method extends existing methods combining a frequency-domain adaptive filter (FDAF) and time-domain PEM-based decorrelation. Such a combined approach is critical, since a standard adaptive feedback cancellation (AFC) procedure without proper decorrelation yields biased estimation results and hence does not provide suitable acoustic feedback control. The proposed modification allows to lower the computational complexity of the algorithm, while maintaining comparable performance.

Adaptive feedback cancellation (AFC) algorithms are used to solve the problem of acoustic feedback, but, frequently, they do not address the fundamental problem of loudspeaker and source signal correlation, leading to an estimation bias if standard adaptive filtering methods are used. Loudspeaker and source signal prefiltering via the prediction-error method (PEM) can address this problem. In addition to this, the use of a frequency-domain Kalman filter (FDKF) is an appealing tool for the estimation of the adaptive feedback canceler, given the advantages it offers over other common techniques, such as Wiener filtering. In this paper, we derive an algorithm employing a PEM-based prewhitening and a frequency-domain Kalman filter (PEM-FDKF) for AFC. We demonstrate its improved performance when compared with standard frequency-domain adaptive filter (FDAF) algorithms, in terms of reduced estimation error, achievable amplification and sound quality.

Feedback oscillation is one of the major problems faced by the hearing aid users. Adaptive feedback cancellation is used to suppress the feedback by using an adaptive filter to estimate the feedback path. However, this estimate is biased due to presence of high correlation between the original input signal and the loudspeaker input signal. Bias problem produces modeling error and can result in cancellation of the desired signal. Bias reduction can be obtained by using band-limited linear predictive coding based technique (BLPC) and band-limited residual excited linear predictive coding (BRELP) based technique. The paper compares performance of band-limited linear predictive coding based vocoder and band-limited residual excited linear predictive coding based vocoder in sound quality and bias reduction. The BLPC-AFC achieves more reduction in high-frequency correlation than BRELP-AFC. However, BRELP vocoder has a better output sound quality.

A proportionate affine projection like (PAPL-I) adaptive filtering algorithm is proposed that incorporates the proportionate feature to affine projection like (APL-I) algorithm. Simulation results show that the proposed PAPL-I algorithm provides improved steady state performance than the APL-I and affine projection algorithms (APA) in case of an acoustic echo cancellation application with a very sparse echo path. In addition, the PAPL-I algorithm also offers improved performance over the APL-I algorithm for adaptive feedback cancellation for hearing aids systems.

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 general feedback cancellation setups, standard adaptive filtering techniques fail to provide a reliable feedback path estimate if the desired signal is spectrally colored because of the presence of a closed signal loop. In this paper, several approaches for improving the estimation accuracy of the adaptive feedback canceller in hearing aids will be reviewed, including constrained adaptation and bandlimited adaptation of the feedback canceller as well as adaptation with the prediction-error method (PEM) using a fixed or adaptive model of the desired signal. Partitioned-block frequency-domain implementations of these algorithms will be compared for acoustic feedback paths measured in two commercial behind-the-ear hearing aids. In addition, it is shown that the tracking performance of the PEM-based feedback canceller with adaptive signal model can be improved by the so-called shadow filter approach known from echo cancellation.

Hearing aid users often suffer from feedback which causes "howling" and limits the maximum stable gain. The direct method of adaptive feedback cancellation is widely used to mitigate feedback, however it is less effective for high forward path gains due to the inherent bias in the feedback path estimate. This bias can be reduced at the expense of artificial delays which can potentially introduce pre-echo and "comb filter" effects. The direct method also tends to cancel tonal audio signals such as alarms and music. We propose using a system identification method in closed-loop for unbiased feedback cancellation which does not possess the negative characteristics manifested by the direct method, but uses an identification signal. The two-stage method which employs two adaptive filters, one to identify the entire closed-loop, and another to extract the feedback path response was the preferred unbiased method due to its low computation and good feedback identification particularly during "howling." This paper presents the theory of the proposed new method, and computer simulations which demonstrate its advantages over the widely used direct method.

The standard continuous adaptation feedback cancellation algorithm for feedback suppression in hearing aids suffers from a large model error or bias if the received sound signal is spectrally colored. To reduce the bias in the feedback path estimate, we propose adaptive feedback cancellation techniques that are based on a closed-loop identification of the feedback path as well as the (auto-regressive) modeling of the desired signal. In general, both models are not simultaneously identifiable in the closed-loop system at hand. We show that-under certain conditions, e.g., if a delay is inserted in the forward path-identification of both models is indeed possible. Two classes of adaptive procedures for identifying the desired signal model and the feedback path are derived: a two-channel identification method as well as a prediction error method. In contrast to the two-channel identification method, the prediction error method allows use of different adaptation schemes for the feedback path and for the desired signal model and, hence, is found to be preferable for highly nonstationary sound signals. Simulation results demonstrate that the proposed techniques outperform the standard continuous adaptation algorithm if the conditions for identifiability are satisfied.

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.

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.

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.

Feedback cancellation in hearing aids based on Filtered-X LMS is analyzed. The data used for identification of the feedback path are the output and input signals of the hearing aid. The identification is thus done in a closed loop. Tracking characteristics and bias of the optimal estimate are discussed. The optimal estimate can be biased when the identification is performed in closed loop and the input signal to the hearing aid is not white. It is shown that the bias could be avoided if the spectrum of the input signal was known and the data used to update the internal feedback is prefiltered. The effects of different choices of the design variables of the Filtered-X LMS are discussed. Three alternatives of the fixed filter are evaluated on feedback paths of hearing aids on human subjects and with alternative spectra of the input signal. The optimal estimate of the Filtered-X LMS with an individually adjusted fixed filter shows the best agreement with the desired estimate.

Hearing aids often suffer from acoustic feedback, which limits the achievable amplification and may severely degrade sound quality by creating howling artifacts. A potential method of feedback cancellation comprises predicting the feedback path (FP) using an adaptive filter. However, a large model error, or bias, is introduced due to signal correlations. To reduce the bias in the FP estimate, a prediction-error-method (PEM) has been used, which is based on a closed-loop identification of the FP and the auto-regressive modeling of the desired signal. This approach, which represents the FP using an FIR filter, requires a large number of parameters. Furthermore, in a reverberant environment, even a high order of the FIR filter may be insufficient to fully represent the FP, which reduces the convergence rate and limits the maximum stable gain of the system. In this contribution, we introduce a PEM-based method that utilizes orthonormal basis functions to precisely predict the acoustic FP in hearing aids. Simulation results with measured data show that the proposed method outperforms the standard PEM adaptation algorithm in terms of convergence rate and maximum stable gain.

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.

Standard adaptive feedback cancellation algorithms in hearing aids suffer from a biased adaptation if the input signal is spectrally colored, as it is for tonal signals, like music. Due to that, distortion artifacts (entrainment) are generated. In this paper, a sub-band feedback cancellation system is presented combined with an adaptation control to deal with those signals. Two control concepts for determining the variable step sizes [1, 2], known from general adaptive filter algorithms, are theoretically and practically analyzed and evaluated for an application to feedback cancellation. For feedback cancellation the control is combined with known methods to reduce the bias, such as prediction error filtering or frequency shifting. Based on this combination, a completely new setup for feedback cancellation is proposed. It relies entirely on signals accessible in real systems, shows a low computational complexity, and therefore has a strong practical relevance.

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 adaptive nonlinear feedback-control method is proposed to control continuous-time chaotic dynamical systems, where the adaptive nonlinear controller acts on only one-dimensional error signals between the desired state and the observed chaotic state of a system. The reduced parameter adaptive quadratic predictor used in adaptive feedback cancellation of the nonlinear terms can control the system at any desired state. Computer simulation results on the Lorenz system are shown to demonstrate the effectiveness of this feedback-control method.

The undesired effects of acoustic feedback of hearing aids can be reduced with an internal feedback path that is an estimate of the external feedback path. This paper analyzes the limiting estimate of the feedback for feedback cancellation schemes that apply some recursive prediction error method with a quadratic norm, e.g., least mean square (LMS) and recursive least squares (RLS), to the output and input signals of the hearing aid to identify the feedback path. The data used for identification are then collected in a closed loop and the estimate used in one recursion will affect the data used in succeeding recursions. These properties have to be considered in the analysis. The analysis shows that the limiting estimate may be biased if there is an error in the used model of the input signal to the hearing aid, and that the system is not identifiable unless a second input signal to the system is added to the output of the hearing aid or the signal processing of the hearing aid used to modify the signal to the impaired ear is nonlinear. The limiting estimate is presented as the solution to an optimization problem in the frequency domain. An analytical expression of the limiting estimate is presented for a special case. For other cases an algorithm is presented that can be used to find a numerical solution. The results can be useful when the model structure used with the recursive identification is chosen.

Acoustic feedback is a problem in hearing aids that contain a substantial amount of gain, hearing aids that are used in conjunction with vented or open molds, and in-the-ear hearing aids. Acoustic feedback is both annoying and reduces the maximum usable gain of hearing-aid devices. This paper studies analytically the steady-state convergence behavior of LMS-based adaptive algorithms when used in continuous adaptation to reduce acoustic feedback. A bias is found in the adaptive filter's estimate of the hearing-aid acoustic feedback path. Methods for reducing this bias and producing an improved estimate of the acoustic feedback path are analyzed and compared. It is shown that by the use of a delay in the forward or cancellation paths of the hearing aid plant, and for representative feedback paths, it is possible to reduce this bias by more than 15 dB.

Variations in the loop response of hearing aids caused by jaw movements, variations in acoustics outside the ear, and variations of vent size have been identified. Behind The Ear (BTE) and In The Ear Canal (ITEC) hearing aids were considered. The largest variations among the variations of the acoustics outside the ear, except when the hearing aid was partly removed, were found with the ITEC when a telephone set was placed by the ear. The variations of the loop response caused by changes in vent size were compared with the variations of a theoretical model of the feedback path. The theoretical model was also used to compare the feedback of different designs of the vent that gives the same acoustic impedance at low frequencies. The calculated feedback was less with the short vents (12 mm) than the long vents (24 mm).

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.

System identification concepts play a key role in adaptive signal processing applications like communication channel estimation, acoustic echo cancellation, active noise control, and adaptive feedback cancellation. In the development of such systems, one would often like to know what kind of performance could be expected and how it depends on the filter length before actually committing the adaptive filter design. Also, after the system has been implemented, it is sometimes necessary to monitor the performance. This paper shows that in such cases, one can infer the potential performance in several ways from input/output measurements of the system, taking into account the obscuring effects of additive noise.

This paper proposes an efficient method for continuous adaptive feedback cancellation (AFC) in digital hearing aids. The AFC is achieved by two filters: an adaptive filter to model and track variations in the acoustic feedback path, and a piecewise-fixed filter for feedback neutralization. The proposed method employs a delay-based technique where an appropriate delay is inserted in the signal flow path and an extended-length filter is used for the AFC. An efficient strategy is proposed to check when the AFC filter reaches the optimal solution. Furthermore, the proposed strategy can detect when there is a strong perturbation in the acoustic path, and hence AFC filter is reinitialized. The computer simulations are carried out to demonstrate the effectiveness of the proposed method. We observe that, as compared with the conventional method, the proposed method gives robust performance even in the case of a strong perturbation in the acoustic path.

We have implemented a same-frequency cellular repeater that uses adaptive signal processing to cancel the feedback path, thereby allowing high gain while maintaining stability. Preliminary results are presented for a real-time laboratory demonstration using an FPGA development board, realizing cancellation of about 20 dB over a 10-MHz bandwidth.

Adaptive Feedback Cancellation (AFC) methods are used to find an FIR filter to cancel the negative effect of acoustic feedback between the loudspeaker and microphone of the hearing aid. Finding the AFC filter of appropriate order/length directly affects the performance and complexity of the system. In this paper, we use noise injection method to find the AFC filter estimating the feedback path model. We show that the optimum length which guarantees a good compromise between the quality and the complexity of the system may be smaller than the length of the actual feedback path model. However, in order to improve the performance of the system in terms of Misalignment criterion, we propose using multiple short-time noise injections and averaging method to find the best filter estimate of appropriate length.

This paper presents the design of fully programmable digital signal processor for the hearing aid system and optimized implementation of digital hearing aid algorithms. The average performance of the developed processor is measured by performing Fast Fourier Transform (FFT) algorithm. The proposed applications such as variable multiband loudness compensation algorithm with wide dynamic range compression and sub-band based adaptive feedback cancellation algorithm are optimized for real-time processing by exploiting the data and instruction parallelism of the developed processor. The results show that the computational ability and power consumption of the developed processor are suitable to use for the hearing aid system. The optimization achieves that the computational time to perform the full applications on the processor takes only under 40% of the constraint for real-time processing.

Five techniques for reducing acoustic feedback in hearing aids were investigated: an adaptive notch filter, three previously described methods for adaptive feedback cancellation, and a novel method for feedback cancellation with adaptation during quiet intervals. Through real-time implementations, these techniques were assessed for added stable gain and sound quality. Test results showed the novel system to provide the best overall performance. >

A same-frequency cellular repeater is proposed that uses adaptive signal processing to cancel the feedback path, thereby allowing high gain while maintaining stability. This technique has application wherever it is desired to boost signal strength in outlying areas of cell coverage, particularly inside homes and buildings. It is intended to provide useful gain without needing special donor/transmit antennas requiring directional gain, separation beyond a meter or so, or roof installation. A simulation of a 900-MHz repeater with 10-MHz bandwidth is presented that demonstrates the feasibility and determines the effect of parameter values on convergence and cancellation performance.

This paper presents a new algorithm for adaptive feedback cancellation (AFC) suitable for hearing aids. A variable step size scheme is added to a step size decorrelated NLMS algorithm. It is shown that the proposed algorithm has increased robustness and stability for both fixed and variable gain cases.

Numerous methods have been proposed to cancel the unpleasant effects of acoustic feedback between the loudspeaker and microphone in hearing aid systems. Adaptive Feedback Cancellation (AFC) methods are often used to estimate an FIR filter for cancelling the feedback path effect. In estimating the AFC FIR filter, it is important to select the order of the filter properly; especially when the feedback path changes from one environment to another and no knowledge about it is available. Choosing improper filter order causes deficient system performance or excessive computations and power usage in the system. We present tracking of the energy of AFC FIR filters and its convergence behavior as a new criterion for determining the proper order for AFC FIR filter. Experimental results show validity of the proposed criterion.

Adaptive filters are widely used in acoustic feedback cancellation systems and have evolved to be state-of-the-art. One major challenge remaining is that the adaptive filter estimates are biased due to the nonzero correlation between the loudspeaker signals and the signals entering the audio system. In many cases, this bias problem causes the cancellation system to fail. The traditional probe noise approach, where a noise signal is added to the loudspeaker signal can, in theory, prevent the bias. However, in practice, the probe noise level must often be so high that the noise is clearly audible and annoying; this makes the traditional probe noise approach less useful in practical applications. In this work, we explain theoretically the decreased convergence rate when using low-level probe noise in the traditional approach, before we propose and study analytically two new probe noise approaches utilizing a combination of specifically designed probe noise signals and probe noise enhancement. Despite using low-level and inaudible probe noise signals, both approaches significantly improve the convergence behavior of the cancellation system compared to the traditional probe noise approach. This makes the proposed approaches much more attractive in practical applications. We demonstrate this through a simulation experiment with audio signals in a hearing aid acoustic feedback cancellation system, where the convergence rate is improved by as much as a factor of 10.

Various methods have been proposed to overcome the problem of compensating the acoustic feedback path that negatively impacts the performance of hearing aid devices. However, in most of them feedback path model is assumed to be fixed which is not quite realistic. In this paper, we consider fixed and variable feedback paths and analyze for each case the performance of one of the robust Adaptive Feedback Cancellation (AFC) schemes, i.e. the Prediction Error Method AFC which uses Partitioned Block Frequency-Domain Normalized Least Mean Square (PBFD-NLMS) algorithm. Based on the analysis results we propose varying the step size values for the same adaptive algorithm on the fly by monitoring the misalignment criteria. The experimental results using the proposed method show improvement made on the system performance.

Feedback oscillation is one of the major issues with hearing aids. An effective way of feedback suppression is adaptive feedback cancellation, which uses an adaptive filter to estimate the feedback path. However, when the external input signal is correlated with the receiver input signal, the estimate of the feedback path is biased. This so-called “bias problem” results in a large modeling error and a cancellation of the desired signal. This paper proposes a band-limited linear predictive coding based approach to reduce the bias. The idea is to replace the hearing-aid output with a synthesized signal, which sounds perceptually the same as or similar to the original signal but is statistically uncorrelated with the external input signal at high frequencies where feedback oscillation usually occurs. Simulation results show that the proposed algorithm can effectively reduce the bias and the misalignment between the real and the estimated feedback path. When combined with filtered-X adaptation in the feedback canceller, this approach reduces the misalignment even further.

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.

Acoustic feedback is a well-known problem in hearing aids, which is caused by the undesired acoustic coupling between the loudspeaker and the microphone. Acoustic feedback limits the maximum amplification that can be used in the hearing aid without making it unstable. The goal of adaptive feedback cancellation (AFC) is to adaptively model the feedback path and estimate the feedback signal, which is then subtracted from the microphone signal. The main problem in identifying the feedback path model is the correlation between the near-end signal and the loudspeaker signal, which is caused by the closed signal loop. A possible solution to this problem is to use the prediction error method (PEM)-based AFC with a linear prediction (LP) model for the near-end signal. In this paper, a modification to the PEM-based AFC is presented where the LP model is replaced by a sinusoidal near-end signal model. More specifically, it is shown that using frequency estimation techniques to estimate the sinusoidal near-end signal model improves the performance of the PEM-based AFC compared to using a LP model. Simulation results for a hearing aid scenario indicate a significant improvement in terms of misadjustment and maximum stable gain increase.

Adaptive filters are commonly used to cancel acoustic feedback in hearing aids. The sound quality of hearing aids deteriorates as the hearing aid gain is increased. This paper presents a method that increases gain possible in hearing aids by automatically identifying and suppressing residual acoustical feedback components that have the potential to drive the system to instability. The increase in the hearing aid gain over traditional methods and the output sound quality were evaluated using subjective tests. The results indicate that the method of this paper provides 6 to 8 dB more hearing aid gain and less perceptual distortion in the output sound quality than several competing algorithms to improve the performance of adaptive feedback cancellers.