Acoustic Feedback Cancellation Research Articles

Acoustic feedback cancellation in digital hearing aids: A sparse adaptive filtering approach

Cancelling the effect of acoustic feedback is a challenging task in the design of a behind the ear digital hearing aid. In traditional behind the ear digital hearing aids, feedback cancellation is usually achieved using an adaptive finite impulse response filter, the weights of which are updated using a suitable learning rule. However, the impulse response of the acoustic feedback path in a hearing aid is sparse in nature and traditional feedback cancellation systems are not designed to utilize this sparseness. An adaptive feedback canceller, which is trained using a set of sparse adaptive algorithms is designed in this paper to take advantage of the sparseness. Further, an attempt has been made to enhance the convergence of the feedback cancellation mechanism by introducing an adaptive de-correlation filter as well as using the concept of probe noise injection. The proposed feedback cancellation schemes are shown to provide improved and accurate feedback cancellation over traditional feedback cancellation mechanisms.

Gain Scheduling of Auxiliary Noise and Variable Step-Size for Online Acoustic Feedback Cancellation in Narrow-Band Active Noise Control Systems

In this paper, we investigate active noise control systems, which use auxiliary white Gaussian noise for online feedback path modeling and neutralization. Such systems comprise three adaptive filters: 1) active noise control filter, 2) linear prediction filter, and 3) feedback path modeling and neutralization filter. The injection of white Gaussian noise with large/small variance improves/degrades the convergence of feedback path modeling and neutralization filter but on the other hand degrades/improves the noise-reduction performance of active noise control systems. The focus of this paper is to have an adequate compromise between the convergence of feedback path modeling and neutralization filter and the noise-reduction-performance of active noise control systems. In this paper: 1) A tuning free gain scheduling of white Gaussian noise is used to improve the noise-reduction performance of active noise control systems, and 2) a variable step-size is used to compensate for the decrease in the convergence of feedback path modeling and neutralization filter due to gain scheduling. The proposed idea is explained for a single channel as well as for multichannel active noise control systems. Computer simulations have been carried out to demonstrate the performance of the proposed method for a single channel as well as for multichannel active noise control systems.

Howling and Entrainment in Hearing Aids: A Review

This review focuses on howling and entrainment artifacts in digital hearing aids. The howling may occur (especially at high gains), essentially due to the close proximity of the input microphone and the output loudspeaker. The entrainment, on the other hand, occurs when the input to the hearing aids is periodic, for example, music signals or alarm signals with strong tonal characteristics. We give details on methods for howling avoidance, which are mainly based on adaptive filtering-based acoustic feedback cancellation. We also give an overview of many recent works on entrainment in hearing aids. Finally, we remark that efficient acoustic feedback cancellation scheme which can avoid howling, can also well manage the entrainment artifact.

An Effective Acoustic Feedback Cancellation Algorithm Based on the Normalized Sub-Band Adaptive Filter

An Effective Acoustic Feedback Cancellation Algorithm Based on the Normalized Sub-Band Adaptive Filter

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

The hearing aid being a battery operated, portable device requires short processing delay, low computational complexity, with appreciable acoustic feedback cancellation effect. The prediction error method (PEM) and PEM with shadow filter (PEM-SH) based adaptive feedback canceller (AFC) referred as PEMAFC and PEMAFC-SH respectively reduces the amount of bias present in the estimate of feedback path. The available partitioned block frequency domain adaptive filter (PBFAF) based implementation of PEMAFC (PBFAF-P) and PEMAFC-SH (PBFAF-PS), offers a potential option for modelling an adaptive filter with many taps along with short block processing delay. However, the PBFAF suffers from large computational load because of the involvement of computationally expensive gradient constraints in each partition. Though removing or alternately applying the gradient constraint saves some computations but it results in significant performance degradation. With an objective of substantially reducing the computational burden and simultaneously retaining the performance, this paper develops an improved partitioned block Hartley domain adaptive filter (IPBHAF) and then employs it for effective feedback cancellation in hearing aids. Further, the IPBHAF with modified step size (IPBHAF-M) is proposed to achieve both fast convergence and better steady state performance. The simulation based experiments demonstrate the superior performance of IPBHAF-M based implementations of PEMAFC (IPBHAF-MP) and PEMAFC-SH (IPBHAF-MPS) over the PBFAF-P and PBFAF-PS in terms of both computational complexity and feedback cancellation performance.

On the use of cepstral analysis in acoustic feedback cancellation

Acoustic feedback limits the maximum stable gain of a public address system. This may make the system unstable resulting in a howling artifact, a phenomenon known as Larsen effect. Acoustic feedback cancellation methods use an adaptive filter to identify the acoustic feedback path and then remove its influence from the system. However, if the traditional gradient-based or least-squares-based adaptive filtering algorithms are used, a bias is introduced in the estimate of the acoustic feedback path because the system input and loudspeaker signals are highly correlated. This work proposes a new method based on cepstral analysis of the error signal to identify the acoustic feedback path in order to reduce the bias problem. Simulation results in one typical acoustic environment demonstrate that, when the source signal is white noise or speech, the presented method can estimate the feedback path with a misalignment of −25dB and increase by 30dB the maximum stable gain of a public address system.

Analysis of Two Microphone Method for Feedback Cancellation

Acoustic feedback cancellation in hearing aids makes use of adaptive filters to continuously identify and track variations to the feedback path. One of the biggest problems remaining in using adaptive filters for feedback cancellation is the biased estimation of the filter's coefficients. In order to remove the undesired correlation between the loudspeaker and incoming signal, a recent alternative scheme proposed to employ an additional microphone. This microphone can provide added information to obtain an incoming signal estimate. This estimate is removed from the primary microphone signal to create the error signal which adapts the canceler's coefficients. This letter provides the theoretical analysis for the two microphone method. It presents analytic expressions showing that the optimal solution is no longer dependent on the signal correlation aforementioned but is now mainly determined by the additional feedback path. Finally, it demonstrates simulation results with the prediction error method in terms of misalignment and maximum gain for a proposed microphone placement. The results show that a more stable solution is obtained with the proposed two microphone approach.

Wiener variable step size and gradient spectral variance smoothing for double-talk-robust acoustic echo cancellation and acoustic feedback cancellation

Double-talk (DT)-robust acoustic echo cancellation (AEC) and acoustic feedback cancellation (AFC) are needed in speech communication systems, e.g., in hands-free communication systems and hearing aids. In this paper, we derive a practical and computationally efficient algorithm based on the frequency-domain adaptive filter prediction error method using row operations (FDAF-PEM-AFROW) for DT-robust AEC and AFC. The proposed algorithm features two main modifications: (a) the Wiener variable step size (WVSS) and (b) the gradient spectral variance smoothing (GSVS). In AEC simulations, the WVSS-GSVS-FDAF-PEM-AFROW algorithm obtains outstanding robustness and smooth adaptation in highly adverse scenarios such as in bursting DT at high levels, and in a change of acoustic path during continuous DT. Similarly, in AFC simulations, the algorithm outperforms state-of-the-art algorithms when using a low-order near-end speech model and in colored non-stationary noise.

Variable step-size affine projection algorithm based on global speech absence probability for adaptive feedback cancellation

A novel approach is proposed for improving adaptive feedback cancellation using a variable step-size affine projection algorithm (VSS-APA) based on global speech absence probability (GSAP). The variable step-size of the proposed VSS-APA is adjusted according to the GSAP of the current frame. The weight vector of the adaptive filter is updated by the probability of the speech absence. The performance measure of acoustic feedback cancellation is evaluated using normalized misalignment. Experimental results demonstrate that the proposed approach has better performance than the normalized least mean square (NLMS) and the constant step-size affine projection algorithms.

가변 스텝 크기 적응 필터와 음성 검출기를 이용한 보청기용 피드백 제거 알고리즘

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.

New Insights Into Optimal Acoustic Feedback Cancellation

In this letter, we present new insights into the bias problem for acoustic feedback cancellation when a probe signal approach is used. The optimum solution of the feedback canceler is not the feedback path but the product of the feedback path and the sensitivity function and hence, the solution is biased. The novelty of this paper also consists of the derivation of the conditions for unbiased feedback cancellation when a probe signal is used as input to the canceler. An adequate delay in the forward path is necessary to reduce, or remove the bias term. The theoretical analysis is verified with simulation results.

Novel Acoustic Feedback Cancellation Approaches in Hearing Aid Applications Using Probe Noise and Probe Noise Enhancement

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.

On Acoustic Feedback Cancellation Using Probe Noise in Multiple-Microphone and Single-Loudspeaker Systems

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.

Transient Mean-Square Analysis of Prediction Error Method-Based Adaptive Feedback Cancellation in Hearing Aids

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.

Low-Delay Signal Processing for Digital Hearing Aids

Digital signal processing in modern hearing aids is typically performed in a subband or transform domain that introduces analysis-synthesis delays in the forward path. Long forward-path delays are not desirable because the processed sound combines with the unprocessed sound that arrives at the cochlea through the vent and changes the sound quality. Nonetheless, subband domain processing for digital hearing aids is the most popular choice for hearing aids because of the associated computational simplicity. In this paper, we present an alternative digital hearing aid structure with low-delay characteristics. The central idea in the paper is a low-delay spectral gain shaping method (SGSM) that employs parallel parametric equalization (EQ) filters. The low-delay SGSM provides frequency-dependent amplification for hearing loss compensation with low forward path delays and performs dynamic signal processing such as noise suppression and dynamic range compression. Parameters of the parametric EQ filters and associated gain values are selected using a least-squares approach to obtain the desired spectral response. The low-delay structure also employs an off-the-forward-path, frequency domain adaptive filter to perform acoustic feedback cancellation. Extensive MATLAB simulations and subjective evaluations of the results indicate that the method of this paper is competent with a state-of-the-art digital hearing aid system, but exhibits much smaller forward-path delays.

A Combined Feedback and Noise Cancellation Algorithm for Binaural Hearing Aids

This paper proposes an adaptive algorithm for the combined acoustic feedback and noise cancellation in the binaural hearing aids. The proposed algorithm is based on dual microphones fo ...

Measurement of hearing aid internal noise

Hearing aid equivalent input noise (EIN) measures assume the primary source of internal noise to be located prior to amplification and to be constant regardless of input level. EIN will underestimate internal noise in the case that noise is generated following amplification. The present study investigated the internal noise levels of six hearing aids (HAs). Concurrent with HA processing of a speech-like stimulus with both adaptive features (acoustic feedback cancellation, digital noise reduction, microphone directionality) enabled and disabled, internal noise was quantified for various stimulus levels as the variance across repeated trials. Changes in noise level as a function of stimulus level demonstrated that (1) generation of internal noise is not isolated to the microphone, (2) noise may be dependent on input level, and (3) certain adaptive features may contribute to internal noise. Quantifying internal noise as the variance of the output measures allows for noise to be measured under real-world processing conditions, accounts for all sources of noise, and is predictive of internal noise audibility.

Acoustic feedback cancellation based on weighted adaptive projection subgradient method in hearing aids

Acoustic feedback is an important factor that degrades the overall performance of hearing aids, and acoustic feedback cancellation has always been the research focus in the field of signal processing in hearing aids. The newly suggested adaptive projection subgradient method (APSM) for adaptive signal processing solves the problem of difficulty in finding the exact projection operator in the realization of affine projection by taking the subgradient projection hyperplane as the searching region for relaxed projection. This work applies APSM in the acoustic feedback cancellation system of hearing aids for the first time, and proposes a weighted adaptive projection subgradient method (WAPSM), which takes into consideration the exponential decay weight factor to incorporate the prior information of estimation system. The new method is compared with the traditional NLMS algorithm and APSM algorithm in simulation experiments. Incorporating the prior information of estimation system by setting the proper weighting matrix, WAPSM achieved notable improvements on the speed, stability and accuracy of the misalignment convergence. Numerical experiments demonstrate that the proposed algorithm is more robust for low SNR and real speech segment input than the traditional algorithms.

Adaptive active noise control incorporating with a transfer function method for reducing acoustic feedback in a duct

In most practical applications of active noise control, the acoustic feedback is a major problem that often interferes with the operation of the control system and even renders it unstable. We propose a frequency-domain method based on transfer function to reduce the influence of acoustic feedback based on plane wave transmission theory of sound in a duct. The original signal of primary noise is obtained from the measured signals of reference microphone and error microphone which are transformed to frequency domain by FFT and operated by the proposed method. In this study, Filtered-X LMS algorithm is applied to carry out the active noise controller incorporating with this transfer function method. Experimental results of active noise control show that the system has achieved 18.5 dB maximal attenuation in the frequency band 200-600 Hz. On the contrary, it is only attenuate 3-4 dB if acoustic feedback is present. Therefore, it was to verify that the proposed method of acoustic feedback cancellation incorporating with the Filtered-X LMS algorithm can effectively reduce the influence of acoustic feedback and obtains a better performance of active noise control in a duct.

Toward bias minimization in acoustic feedback cancellation systems

A novel technique for bias suppression within acoustic feedback cancellation systems is proposed. This is achieved based on the use of all-pass filters in the forward part of the hearing aid. The poles of these filters are made time-varying, which results in a frequency response with constant magnitude and varying phase. This is a desired feature of the proposed approach, since the results from human psychoacoustics show that the human ear is not sensitive to moderate phase perturbations. The derivation of the proposed algorithms for the time variation of the location of the poles of all pass filters is based on a rigorous analysis of the phenomenon of bias in acoustic systems. Practical issues, such as the dependence of the steady-state error on the order of the all-pass filter, the number of varying poles, and their standard deviation are examined and strategies for the variation of the poles are introduced. Results obtained from a simulated hearing aid are provided to support the analysis. The quality of the processed audio signals is evaluated through subjective tests.