Control of feedback for assistive listening devices

Acoustic feedback refers to the undesired acoustic coupling between the loudspeaker and microphone in hearing aids. This feedback channel poses limitations to the normal operation of hearing aids under varying acoustic scenarios. This work makes contributions to improve the performance of adaptive feedback cancellation techniques and speech quality in hearing aids. For this purpose a two microphone approach is proposed and analysed; and probe signal injection methods are also investigated and improved upon

Analysis of acoustic feedback cancellation systems based on direct closed-loop identification

This work presents, using the least squares estimation theory, a theoretical and experimental analysis on the performance of the standard adaptive filtering algorithms when applied to acoustic feedback cancellation. Expressions for the bias and covariance matrix of the acoustic feedback path estimate provided by these algorithms are derived as a function of the signals statistics as well as derivatives of the cost function. It is demonstrated that, in general, the estimate is biased and presents a large covariance because the closed-loop nature of the system makes the cross-correlation between the loudspeaker and system input signals non-zero. Simulations are carried out to exemplify the results using speech signals, a long acoustic feedback path and the recursive least squares algorithm. The results illustrate that these algorithms converge very slowly to a solution that is not the true acoustic feedback path. The relationship between the performance of the adaptive filtering algorithms and the aforementioned cross-correlation is proven by varying the signal-to-noise ratio and the delay introduced by the forward path.info:eu-repo/semantics/publishedVersio

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

Feedback cancellation with probe shaping compensation

Adaptive feedback cancellation methods may integrate the use of probe signals to assist with the biased optimal solution in acoustic systems working in closed-loop. However, injecting a probe noise in the loudspeaker decreases the signal quality perceived by users of assistive listening devices. To counter this, probe signals are usually shaped to provide some level of perceptual masking. In this letter we show the impact of using a shaping filter on the system behavior in terms of convergence rate and steady state error. From this study, it can be concluded that shaping the probe signal may have detrimental influence in terms of system performance. Accordingly, we propose to use the unshaped probe signal combined with an inverse filter of the shaping filter to identify the feedback channel. This restructure of the problem restores convergence rate of LMS type algorithms. Furthermore, we also show that an adequate forward path delay is required to obtain an unbiased solution and that the suggested scheme reduces this delay

Partitioned block frequency domain prediction error method based acoustic feedback cancellation for long feedback path

In this paper an innovative method of using Acoustic Feedback Cancellation (PEM based PBFD-AFC) in large acoustic spaces is presented. The system under analysis could vary from Single Source Single Receiver (SISR) to a Multiple Sources Multiple Receivers (MSMR). An environment is representative of (e.g.) churches installations or Public Address (PA) systems, thus involving the presence of one or more microphones and corresponding feedback paths. The Partitioned Block approach consists of slicing the feedback path (e.g the impulse response of the system) to improve the algorithm performance. It can be applied either in the time domain or in the frequency domain, where the latter, called Partitioned Block Frequency Domain, shows faster convergence, lower computational cost and higher estimation accuracy. The results of the proposed framework is compared with the state of the art using real acoustic data showing superior performance with up to 20dB Maximum Stable Gain (MSG) and 30 seconds less convergence time

Mean square performance evaluation in frequency domain for an improved adaptive feedback cancellation in hearing aids

We consider an adaptive linear prediction based feedback canceller for hearing aids that exploits two (an external and a shaped) noise signals for a bias-less adaptive estimation. In particular, the bias in the estimate of the feedback path is reduced by synthesizing the high-frequency spectrum of the reinforced signal using a shaped noise signal. Moreover, a second shaped (probe) noise signal is used to reduce the closed-loop signal correlation between the acoustic input and the loudspeaker signal at low frequencies. A power-transfer-function analysis of the system is provided, from which the effect of the system parameters and adaptive algorithms [normalized least mean square (NLMS) and recursive least square (RLS)] on the rate of convergence, the steady-state behaviour and the stability of the feedback canceller is explicitly found. The derived expressions are verified through computer simulations. It is found that, as compared to feedback canceller without probe noise, the cost of achieving an unbiased estimate of the feedback path using the feedback canceller with probe noise is a higher steady-state misadjustment for the RLS algorithm, whereas a slower convergence and a higher tracking error for the NLMS algorithm