Variable step-size of the least-mean-square algorithm for reducing acoustic feedback in 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.

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

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.

Step size and tap length play critical roles in balancing the complexity and steady-state performance of an adaptive filter. For an impulse response with an exponential power decay profile, which models a wide range of practical systems, such as an acoustic echo path, this paper proposes a new variable step-size and tap-length least mean square (LMS) algorithm. In each iteration, the optimal step-size and tap-length are derived by minimizing the mean square deviation (MSD) between the true and the estimated filter coefficients. The proposed algorithm performs better in terms of both convergence rate and steady-state performance than the existing ones. Effectiveness of the proposed algorithm is demonstrated through computer simulations.

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.

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.

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

This paper proposes a new application of fuzzy logic to variable step size least mean square (VS-LMS) adaptive beamforming algorithm in CDMA systems. The proposed algorithm adjusts the step size of the least mean square (LMS) by using the application of fuzzy logic in which the increase or decrease of step size depends on the fuzzy inference results of the mean square error (MSE). Computer simulation results show that the proposed algorithm has a better capacity of tracking compared with the conventional LMS algorithms and other variable step size LMS algorithms.

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.

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.

Delta Sigma Analog to Digital Converters (ADCs) are superior to their counterparts in achieving high resolution for low to medium bandwidth applications. This paper discusses an improved digital calibration technique for enhancing Signal to Noise ratio (SNR) of Delta Sigma ADC which is directly related to Effective Number of Bits (ENOB). The proposed method uses the Adaptive Line Enhancer (ALE) for improving the SNR of the modulator. Adaptive Line Enhancer filters out the estimate of signal of interest from noisy input signal by updating filter coefficients using Least Mean Square (LMS) Algorithm. Variable step size (VSS) LMS is used for updating filter coefficients in Adaptive Line Enhancer instead of Fixed step size (FSS) LMS to reduce the required number of iterations. The simulation results for second order single loop Delta Sigma modulator show approximately 20dB improvement in SNR after calibration and substitution of VSS LMS with FSS LMS has reduced approximately 4000 iterations.

Hearing aids have fundamental disadvantages: (1) stigmatization of the patient; (2) the sound is often found to be unsatisfactory due to the limited frequency range and undesired distortion; (3) in many patients, the ear canal fitting device generally necessary leads to an occlusion effect; (4) acoustic feedback when amplification is high. Conventional hearing aids transmit sound into the ear canal via a small microphone. Sound has the disadvantage of requiring high output sound pressure levels for its transmission. This along with the necessary miniaturization of the loudspeaker as well as the resonances and reflections in the closed ear canal contribute to the disadvantages mentioned. In contrast, implantable hearing aids do not make sound signals but micromechanical vibrations. An implantable hearing aid has an electromechanical transducer instead of the loudspeaker of a conventional hearing aid. The hearing signal does not leave the transducer as sound but as a mechanical vibration which is directly coupled to the auditory system bypassing the air. This implantable hearing aid is either coupled to the tympanic membrane, the ossicular chain, the perilymph of the inner ear, or the skull. An implantable hearing aid is expected to have: 1 Better sound fidelity than a hearing aid 2 No ear canal fitting device, free ear canal 3 No feedback 4 Invisibility Requirements on electronic hearing implants designed for patients with conductive hearing loss differ from those on implants for sensorineural hearing loss. Conductive hearing loss requires the implant to replace the impedance transformation, thus being an impedance transformation implant (ITI). In various respects, the demands on an ITI are lower than the demands on an electronic hearing aid for patients with sensorineural hearing loss. The latter are mostly patients with a failure of the cochlea amplifier (CA). A damage to the CA is clinically discernible by a positive recruitment and loss of otoacoustic emissions (OAE). Since these patients form the majority of cases with sensorineural hearing loss, an active hearing implant for such patients should partially replace the function of the CA. Therefore, the suggestion is to refer to a CAI (cochlea amplifier implant). The implant expressions ITI (for patients with conductive hearing loss) and CAI (for patients with sensorineural hearing loss) used in this context allow nomenclatural association with the CI (cochlear implant) for complete inner ear failure as well as with the BSI (brainstem implant) in the case of hearing nerve failure.

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.

The step size of least mean square (LMS) algorithm is significant for its performance. To be specific, small step size can get small excess mean square error but results in slow convergence. However, large step size may cause instability. Many variable step size least mean square (VSSLMS) algorithms have been developed to enhance the control performance. In this paper, a new VSSLMS was proposed based on Kwong’s algorithm to evaluate the robustness. The approximate analysis of dynamic and steady-state performance of this developed VSSLMS algorithm was given. An active vibration control system of piezoelectric cantilever beam was established to verify the performance of the VSSLMS algorithms. By comparing with the current VSSLMS algorithms, the proposed method has better performance in active vibration control applications.