Extracting the invariant model from the feedback paths of digital hearing aids

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.

Feedback is a problem in hearing aids which will cause signal degradation and reduce the maximum applicable gain. More specifically, the advantages of open fittings (e.g., minimizing the occlusion effect) are limited by acoustic feedback. Feedback cancelation algorithms are used to overcome these limitations. For the development of such algorithms, the acoustic feedback path of the hearing aid must be known. The acoustic feedback path is not only affected by the outer sound field but by the individual anatomy and physiology as well. In order to quantify these different influences, feedback path measurements were performed on 20 human subjects. The measurements included different static conditions as well as dynamic ones (i.e., repetitive movements were performed during the measurement). Since the sound pressure level must be limited in measurements on human subjects, a valid identification of the feedback path is difficult in many cases, due to a low signal to noise ratio. Therefore, all measurements were performed reciprocally in addition to the direct measurements. Results show that yaw movements only have a small influence on the acoustic feedback path, whereas changes of the outer sound field can have a substantial impact on the feedback path.

Acoustic feedback is the main problem associated with (broadband) active noise control (ANC) in feed-forward configuration. To solve this problem, auxiliary noise is injected into the ANC system to have accurate model of the feedback path. The conventional approaches substantially model the feedback path at the cost of degradation in the noise reduction performance (NRP) and vice versa. Here, we propose a fuzzy logic-based gain scheduling (of auxiliary noise) for online feedback path modeling and neutralization (FBPMN). The fuzzy controller computes the instantaneous gain for auxiliary noise based on two inputs, the first input gives information about modeling status of FBPMN filter while the second input gives information regarding variance of the disturbance signal for the adaptive FBPMN filter. Furthermore, variable step-size (VSS) is used for the adaptation of FBPMN filter that uses a larger value of step-size during modeling (phase) of FBPMN filter for fast adaptation and a smaller value when the feedback path has been (adequately) modeled. Fuzzy gain scheduler together with VSS in FBPMN filter improves both the NRP and feedback path modeling characteristics. Moreover, the proposed method does not require initial offline modeling of the feedback path thereby demonstrating complete online operation. To make the system computationally efficient, a look-up table-based approach is also proposed in this paper. Simulations results are presented to demonstrate the effectiveness of the proposed method.

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.

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.

Adaptive feedback cancellation in hearing aids

Real-World Benefits of Hearing Aids Beyond Better Speech Understanding

Criteria for estimating an FIR filter for cancelling the feedback path signal in hearing aid system

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.

When fitting hearing aids, there are three guidelines that should always be followed by the dispenser: restore audibility; so that the amplified sound is above the user's threshold, limit the output; so that the amplified signal does not exceed the user's discomfort level, and do no harm; so that the amplified signal is not unintentionally or undesirably altered by the hearing aid. The first two of these three guidelines are obvious. The first is to present sounds above the user's threshold of hearing in order to create an acoustic environment with the maximum amount of audible speech cues. The second ensures that discomfort level is not exceeded, so that the user does not wince or remove the hearing aids when loud sounds are present. The third guideline—do no harm—is less obvious. This indicates that it is important that the hearing aids produce no alteration of the signal other than that which the fitter intends. This is a subtle way of saying that, among other things, well-fitted hearing aids should not distort the desired sound. The topic of distortion is the subject of this issue of Trends in Amplification. The intention of this issue is to explain why distortion occurs in some hearing aids; to explain how distortion is measured; to summarize what can be done to prevent distortion; and to summarize the perception of distortion by the hearing aid wearer. Distortion in hearing aids can be broadly defined as the generation of undesired audible components that are present in the output, but which are not present in the input. When distortion occurs, hearing aids produce undesired elements at the output through the interaction of the processed signal with some internal non-linear mechanism. These undesired components may interfere to some degree or other with the reception of sound by the listener. If these added elements are small compared to the overall signal level, they may effectively cause no interference at all. If they are large, they can be so disruptive to the listener that the desired sound becomes irritating or even incomprehensible. All audio systems inevitably contain some amount of distortion. The practical problem for the listener is the type of distortion that is present and the level that is acceptable or tolerable in hearing aids before the distortion becomes disruptive to speech intelligibility and sound quality. It is known that highly distorted speech in quiet can remain intelligible (Licklider, 1946); however, as dispensers often experience, intelligibility is not the only measure of whether or not a user will accept and wear hearing aids. Gabrielsson and Sjogren (1979b) have shown that overall perceived sound quality is important to users when selecting a hearing aid. Punch (1978) has shown that listeners with mild to moderate sensorineural hearing losses retain the same ability to make distinctions in sound quality judgments as listeners with normal hearing. This implies that good sound quality is as equally important and desired by listeners with a hearing impairment as it is for listeners with normal hearing. Three characteristics of distortion in hearing aids modify the sound delivered to the user: the type of distortion, the relative amount of distortion at different frequencies, and the variation of distortion with different input levels. Since the most important goal for fitting hearing aids is to restore or facilitate communication ability, undistorted sound is important for optimum speech intelligibility and sound quality. However, it is important to also recognize that there are other reasons to provide undistorted amplification of sound. For example, undistorted and pleasant reproduction of music may be an important sensory experience for some listeners. This issue concentrates on different types of undesired modification to the waveshape of the sound, primarily by total harmonic distortion (THD) and intermodulation distortion (IMD). However, in a more general sense, any undesired component added to the sound by hearing aids is a form of distortion. Thus, this issue also discusses sound generated by hearing aids with no signal present at the microphone. This artifact is more commonly called internal noise. Internal noise fits within the definition of distortion given earlier, since internal noise is an undesired product generated in the output of hearing aids that is not present at the input. In some cases, distortion and internal noise are so closely linked that they cannot always be separated into discrete entities. For example, instability in the frequency and shape of the clock pulses in a Class D output stage (often loosely called clock jitter) results in distortion of the signal. However, this artifact may be audibly revealed in the output sound primarily as noise, rather than as distortion of the waveform. According to this broad definition of distortion, undesired noises such as “motorboating” and other acoustic feedback oscillations can also be considered to be forms of distortion. However, acoustic feedback and other similar audible artifacts occurring within hearing aids will not be discussed here. These types of distortion have been discussed in detail by Agnew (1996b) in a previous issue of Trends in Amplification. In this issue distortion is discussed in two general categories: Undesired modification of the waveform of the incoming sound by some mechanism occurring within the hearing aids. The severity of this effect may or may not vary with the acoustic level of the incoming sound. This type of audible artifact is generally what most dispensers refer to as “distortion”. This will be the subject of Part I of this issue, and will follow the definition used for distortion in this text. The production of undesired audible components in the output sound, regardless of whether or not there is any acoustic input to the hearing aid. This is generally called “noise”. This will be the subject of Part II of this issue. The first part of this issue discusses the causes and effect of distortion. First, different types of distortion are described, followed by an explanation of how they are measured. This is followed by a discussion of the sources of distortion that may be present in hearing aids and the various mechanisms by which distortion is created. The final section of Part I discusses the effects of distortion on the hearing aid wearer. For the reader having difficulty understanding the technical concepts in the first part of this tutorial, a good overview from a different perspective has been presented by Kuk (1996). The second part of this issue discusses the causes and effects of internal noise in hearing aids. This part starts with a discussion of sources of internal noise and continues with a description of methods for the measurement of internal noise. This part concludes with a discussion of the perception of internal noise and its effects on the user of hearing aids. At the end of the text is an intentionally long list of references, which is intended to serve as a reasonably comprehensive resource for the reader seeking further information on distortion and noise in hearing aids.

A challenge in hearing aids is adaptive feedback control which often uses an adaptive filter to estimate the feedback path. This estimate of the feedback path usually results in a bias due to the correlation between the loudspeaker signal and the incoming signal. The prediction error method (PEM) is a popular method for reducing this bias for adaptive feedback control (AFC) in hearing aids, providing a significant performance improvement compared to conventional adaptive feedback control techniques. However, the PEM-based AFC (PEM-AFC) applications are still limited to single-microphone single-loudspeaker (SMSL) systems. This paper investigates the application of the PEM-AFC to a two-microphone single-loudspeaker hearing aid with detailed theoretical analysis as well as practical experiments. In the proposed method, PEM-AFC2, we use the two-microphone adaptive feedback control (AFC2) method with two microphones and one loudspeaker. The incoming signals at the two microphones are related by a relative transfer function (RTF) which is used to predict the incoming signal at the main microphone. In addition, a prefilter is employed to prewhiten the loudspeaker and the microphone signals before the adaptive filter estimates. As a result, the proposed method obtains a lower bias and a faster tracking rate compared to the PEM-AFC and the AFC2 method, while still maintaining a good quality of the incoming signal. A new derivation for optimal filters in the AFC2 method will also be provided. The performance of the proposed method is evaluated for speech shaped noise as incoming signal and with undermodeling the RTF as well as with perfect modeling the RTF. Moreover, different types of incoming signals and a sudden change of feedback paths are also considered. The experimental results show that the proposed approach yields a significant performance improvement compared to existing state-of-the-art AFC methods such as the PEM-AFC and the AFC2.

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

To improve the adjustment of the settings of a hearing aid or a hearing instrument respectively, a method is proposed according to which at least one setting of the hearing aid or instrument respectively for a particular acoustic environment is changed or deviated from the actual setting without any preceding action or manipulation of the user to provoke the user to interact or to readjust the setting.

A new subband feedback cancellation scheme is proposed, capable of providing additional stable gain without introducing audible artifacts. The subband feedback cancellation scheme employs a cascade of two narrow-band filters Ai(Z)and Bi(Z)ng with a fixed delay, instead of a single filter Wi(Z)and a delay to represent the feedback path in each subband. The first filter, Ai(Z), is called the training filter, and models the static portion of the feedback path in ith subband, including microphone, receiver, ear canal resonance, and other relatively static parameters. The training filter can be implemented as a FIR filter or as an IIR filter. The second filter, B?I?(Z), is called a tracking filter and is typically implemented as a FIR filter with fewer taps than the training filter. This second filter tracks the variations of the feedback path in the i?th? subband caused by jaw movement or objects close to the ears of the user.