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

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

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

In this paper we propose a new variable step-size of the Least-Mean-Square (LMS) algorithm for reducing acoustic feedback in hearing aids. The new variable step-size is designed to approximate the optimum variable step-size that allows the LMS algorithm to decrease its mean-squared error with the fastest rate. The performance of the proposed variable step-size on the acoustic feedback cancellation is compared via computer simulation with those of three other step-size adjustment methods. The comparisons indicate that the new step-size adjustment method has better dynamic and steady state performances than the other methods. In addition, the computation complexity of the new method is relatively low compared with those of the other methods, rendering it more suitable for solving acoustic feedback in hearing aids.

Affine projection algorithms (APA) have been widely employed for acoustic echo cancellation (AEC) since they provide a natural trade-off between convergence speed and computational complexity. However, their application in Acoustic Feedback Cancellation (AFC) in hearing aids so far has been limited due to lack of performance improvement in one microphone settings. A two microphone technique was recently proposed to provide an improved cancellation for a larger class of input signals. This paper proposes to use APA in the two microphone closed-loop feedback cancellation. It is shown that APA has significantly improved the misalignment and maximum stable gain of the system. Moreover, a new variable Gaussian step-size control (VGSS) for APA is also proposed. The simulation results indicate an improvement in convergence speed of the proposed algorithms as compared to previously suggested methods.

Publication in the conference proceedings of EUSIPCO, Poznan, Poland, 2007

Developing performing speech reinforcement systems to improve the intra-cabin communication quality among car passengers in different row seats, typically degraded by the distance between speakers (for instance in SUV and minivan) and the noise presence within the cockpit, has represented a challenging issue within the related scientific community. One of the main problem to solve in this scenario is the reduction of the electroacoustic coupling between louds and mics in order to avoid the system reaching instability (howling), namely acoustic feedback cancellation (AFC). One of the most performing technique for AFC is the PEM-AFROW approach, recently appeared in the literature. In this work, we propose an innovative feedback suppressor scheme based on the PEM-AFROW concept, which allows to achieve a valuable balance of feedback reduction, maximum stable gain values and overall sound quality. Results obtained by computer simulations in the dual channel communication scenario confirm the effectiveness of the idea.

This study introduces a novel online censoring-least mean square (OC-LMS) based acoustic feedback cancellation (AFC) system to eliminate undesired acoustic feedback that causes instability in hearing aids. The performance of the proposed OC-LMS based AFC system is tested on a sample feedback path measured from a behind-the-ear hearing aid and compared with its conventional LMS based version in terms of mean square deviation, misalignment, and added stable gain metrics. Simulation results shows that the proposed AFC system works at higher performance than its LMS based version with fewer data by censoring noninformative data thanks to its censoring mechanism. The proposed novel system will pave the way for the design of wearable hearing aids that work with both high performance and long battery life.

Bickel and Ritov suggested an optimal estimator for the integral of the square of the kth derivative of a density when the unknown density belongs to a Lipschitz class of a given order $\beta$. In this context optimality means that the estimate is asymptotically efficient, that is, it has the best constant and rate of risk convergence, whenever $\beta > 2k + 1/4$, and it is rate optimal otherwise. The suggested optimal estimator crucially depends on the value of $\beta$ which is obviously unknown. Bickel and Ritov conjectured that the method of cross validation leads to a corresponding adaptive estimator which has the same optimal statistical properties as the optimal estimator based on prior knowledge of $\beta$. We show for probability densities supported over a finite interval that when $\beta > 2k + 1/4$ adaptation is not necessary for the construction of an asymptotically efficient estimator. On the other hand, it is not possible to construct an adaptive estimator which has the same rate of convergence as the optimal nonadaptive estimator as soon as $k < \beta \leq 2k + 1/4$.

In this paper, we propose a novel solution for continuous acoustic feedback neutralization in the digital hearing aids. The proposed method is based on two adaptive filters working in tandem. The error signal of the first adaptive filter is used as a desired response for the second adaptive filter being excited by a low-level (constant) probe signal. Initially at the start up, the first adaptive filter gives a fast convergence, however, its input and desired response are correlated with each other and it may converge to a biased solution at the steady-state. On the other hand, the second adaptive filter, though converging slowly being excited by a low-level probe signal, would give a good steady-state estimate of the acoustic feedback path. An appropriate delay is inserted at the output of the hearing aid, and based-on this delay-based processing, a novel strategy is proposed to exchange the weights between the two adaptive filters. The proposed strategy is such that the misconvergence of the first adaptive filter is avoided, as well as the initial convergence of the second adaptive filter is improved. Extensive computer simulations are carried out which demonstrate the effectiveness of the proposed method.

An enhanced affine projection algorithm (APA) is proposed to improve the filter performance in aspects of convergence rate and steady-state estimation error, since the adjustment of the input-vector number can be an effective way to increase the convergence rate and to decrease the steady-state estimation error at the same time. In this proposed algorithm, the input-vector number of APA is adjusted reasonably at every iteration by comparing the averages of the accumulated squared errors. Although the conventional APA has the constraint that the input-vector number should be integer, the proposed APA relaxes that integer-constraint through a pseudo-fractional method. Since the input-vector number can be updated at every iteration more precisely based on the pseudo-fractional method, the filter performance of the proposed APA can be improved. According to our simulation results, it is demonstrated that the proposed APA has a smaller steady-state estimation error compared to the existing APA-type filters in various scenarios.

This paper presents a hybrid active noise cancelling (HANC) algorithm with online secondary path estimation, together with its evaluation under acoustic feedback distortion present in most feedforward ANC system. The HANC system provides a suitable solution to two important problems present in most ANC systems. The acoustic feedback is reduced using two FIR adaptive filters with feedforward and feedback configurations, respectively; together with the online secondary path modeling. Computer simulation results show that the HANC structure provides a good acoustic feedback cancellation, while the secondary path estimation method performs fairly well, providing better cancelation performance than other previously proposed structures.

The smart antenna adaptive algorithms achieve the best weight vector for beam forming by iterative means. Whether the algorithm is good depends on the convergence rate and steady state error. Beam forming is directly determined by the two factors. The performance of the traditional LMS algorithm is analyzed in this paper. Then a new variable step size algorithm is proposed and is applied to beam forming with the software Matlab. The simulation result indicates that the algorithm improved could achieve faster convergence and lower steady state error.

This paper aims at exploring an adaptive fuzzy dynamic surface control (DSC) with prespecified tracking performance for a class of nonlinear systems in strict‐feedback form. Incorporating DSC technique into fuzzy logic systems (FLSs), it is shown that the design procedure and the computational burden can be greatly reduced. Moreover, by introducing a performance function in controller design, the prespecified tracking performance, i.e. the convergence rate, the allowed maximum overshoot and the steady state error, can be achieved. Simulation results are presented to demonstrate the efficiency of the proposed scheme.Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

The adaptive kernel algorithms usually achieve a good convergence performance and a tracking performance due to the universal approximator, offering an excellent solution to many problems with nonlinearities. However, as is well known, the convergence rate and steady-state error of adaptive filtering algorithm are a pair of inherent contradictions, and the kernel method is not exceptional. For this problem, a robust kernel adaptive filtering algorithm, called the variable-scaling factor kernel fractional lower power adaptive filtering algorithm based on the Sigmoid function, is developed by creating a new framework of cost function which combines the kernel fractional low power error criterion with the Sigmoid function for system identification of different noise environments. This new cost framework incorporates a scaling factor into the cost function of the Sigmoid kernel fractional lower power adaptive filtering algorithm (VS-SKFLP) in this paper. One of the main features in the new framework is its scaling factor. This scaling factor is used to control the steepness of the Sigmoid function, and the steepness can affect the convergence speed of filtering algorithm. The scaling factor provides a tradeoff between the convergence rate and the steady-state mean square error (MSE), which improves the convergence rate under the same steady-state mean square error. However, it is also an important problem to choose an appropriate scale factor. Therefore, a variable-scale factor SKFLP algorithm is also proposed to improve the convergence rate and steady-state MSE, simultaneously. The proposed variable-scale factor structure consists of a function of error, featuring the adaptive updates of their parameter estimated by making discerning use of the error. In this paper, the nonlinear saturation characteristic of the Sigmoid function and low order norm criterion are used to overcome the performance degradation of training data destroyed by non-Gaussian impulse noise and colored noise. Through the convergence analysis, the parameter estimation sequence of our proposed algorithm proves convergent. Simulation results show that the proposed algorithm (VS-SKFLP) outperforms other kernel adaptive filtering algorithms in system recognition with different noise environments.

Acoustic feedback in hearing aids occurs due to the coupling between the hearing aid loudspeaker and microphones. In order to reduce acoustic feedback, adaptive filters are often used to estimate the feedback path. To increase the convergence speed and decrease the computational complexity of the adaptive algorithms, it has been proposed to split the acoustic feedback path into a time-invariant fixed part and a time-varying variable part. A key question of this approach is how to determine the fixed part. In this paper, two approaches are investigated: (1) a digital filter design approach that makes use of the signals of at least two hearing aid microphones and (2) a defined physical location approach using an electro-acoustic model and the signals of one hearing aid microphone and an additional ear canal microphone. An experimental comparison using measured acoustic feedback paths showed that both approaches enable one to reduce the number of variable part coefficients. It is shown that individualization of the fixed part increases the performance. Furthermore, the two approaches offer solutions for different requirements on the effort to a specific hearing aid design on the one hand and the effort during the hearing aid fitting on the other hand.