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

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

Adaptive Feedback Cancellation in Hearing Aids

Acoustic feedback is a well-known phenomenon in hearing aids and public address systems. Under certain conditions it causes the so-called howling effect, which is highly annoying for the hearing aid user and limits the maximum amplification of the hearing aid. The most common choice to prevent howling is the adaptive feedback cancellation algorithm, which is able to completely eliminate the feedback signal. However, standard adaptive feedback cancellation algorithms suffer from a biased adaptation if the input signal is spectrally colored, as it is for speech and music signals. Due to this bias distortion artifacts (entrainment) are generated and consequently, the sound quality is significantly reduced. Most of the known methods to reduce the bias have focused on speech signals. However, those methods do not cope with music, since the tonality and correlation are much stronger for such signals. This leads to a higher bias and consequently, to stronger entrainment for music than for speech. Other methods, which deal with music signals, work only satisfactorily when using a very slow adaptation speed. This reduces the ability to react fast to feedback path changes. Hence, howling occurs for a longer time when the feedback path is changing. In this thesis, a new sub-band adaptive feedback cancellation system for hearing aid applications is proposed. It combines decorrelation methods with a new realization of a non-parametric variable step size. The adaptation is realized in sub-bands which decreases the computational complexity and increases the adaptation performance of the system simultaneously. The applied decorrelation methods, prediction error filter and frequency shift, are well known approaches to reduce the bias. However, the combination of both is first proposed in this thesis. To apply the proposed step size in the context of adaptive feedback cancellation, a method to estimate the signal power of the desired input signal, i.e., without feedback, also referred to as source signal power is necessary. This estimate is theoretically derived and it is demonstrated that it is a reliabe estimate if the decorrelation methods are additionally applied. In order to further improve the performance of the system three additional control methods are derived: The first one is an impulse detection to detect wideband impulses, which could lead to misadaptation. Secondly, a modified estimate of the source signal power to stabilize the system in case of jarring voices is proposed. Lastly, a correlation detection, which is applied to improve the trade-off between adaptation stability and tracking behavior, is developed. The complete system is optimized and evaluated for several speech and music signals as well as for different feedback scenarios in simulations with feedback paths measured under realistic situations. Additionally, the system is tested by real-time simulations with hearing aid dummies and a torso and head simulator. For both simulation setups hearing loss compensation methods as applied in realistic hearing aids are used. The performance is measured in terms of being able to prevent entrainment (adaptation stability) and reacting to feedback path changes (tracking behavior). The complete adaptive feedback cancellation system shows an excellent performance. Furthermore, the system relies only on few parameters, shows a low computational complexity, and therefore has a strong practical relevance

Acoustic Feedback Noise Cancellation in Hearing Aids Using Adaptive Filter

To enhance speech intelligibility for people with hearing loss, hearing aids will amplify speech using gains derived from evidence-based prescriptive methods, in addition to other advanced signal processing mechanisms. While the evidence supports the use of hearing aid signal processing for speech intelligibility, these signal processing adjustments can also be detrimental to hearing aid sound quality, with poor hearing aid sound quality cited as a barrier to device adoption. In general, an uncontrolled environment may contain degradation components like background noise, speech from other speakers etc. along with required speech components. In this paper, we implement adaptive filtering design for acoustic feedback noise cancellation in hearing aids. The adaptive filter architecture has been designed using normalized least mean square algorithm. By using adaptive filters both filter input coefficients are changeable during run-time and reduce noise in hearing aids. The proposed design is implemented in matlab and the simulations shows that the proposed architecture produces good quality of speech, accuracy, maintain stable steady state. The proposed design is validated with parameters like Noise Distortion, Perceptual Evaluation of Speech Quality, Signal to Noise Ratio, and Speech Distortion. The feedback canceller is implemented in MATLAB 9.4 simulink version release name of R2018a is used for validation with Echo Return Loss Enhancement (ERLE). The ERLE of the NMLS is reduced when the filter order is increases. Around 10% of the power spectrum density (PSD) is less when compared with existing designs

Acoustic Feedback Noise Cancellation in Hearing Aids Using Adaptive Filter

To enhance speech intelligibility for people with hearing loss, hearing aids will amplify speech using gains derived from evidence-based prescriptive methods, in addition to other advanced signal processing mechanisms. While the evidence supports the use of hearing aid signal processing for speech intelligibility, these signal processing adjustments can also be detrimental to hearing aid sound quality, with poor hearing aid sound quality cited as a barrier to device adoption. In general, an uncontrolled environment may contain degradation components like background noise, speech from other speakers etc. along with required speech components. In this paper, we implement adaptive filtering design for acoustic feedback noise cancellation in hearing aids. The adaptive filter architecture has been designed using normalized least mean square algorithm. By using adaptive filters both filter input coefficients are changeable during run-time and reduce noise in hearing aids. The proposed design is implemented in matlab and the simulations shows that the proposed architecture produces good quality of speech, accuracy, maintain stable steady state. The proposed design is validated with parameters like Noise Distortion, Perceptual Evaluation of Speech Quality, Signal to Noise Ratio, and Speech Distortion. The feedback canceller is implemented in MATLAB 9.4 simulink version release name of R2018a is used for validation with Echo Return Loss Enhancement (ERLE). The ERLE of the NMLS is reduced when the filter order is increases. Around 10% of the power spectrum density (PSD) is less when compared with existing designs

Adaptive gain processing to improve feedback cancellation in digital hearing aids

Journal ArticleAdaptive filters are commonly used to cancel acoustic feedback in hearing aids. The sound quality of hearing aids deteriorates as the hearing aid gain is increased. This paper presents a method to alter the gain function in digital hearing aids to provide additional amplification and better output sound quality. This approach employs a variable, frequency-dependent gain function that is lower at frequencies of the incoming signal where the information is perceptually insignificant. The increase in stable gain over traditional methods and the output sound quality were evaluated with a psychoacoustic experiment on normal-hearing listeners. The results indicate that the method of this paper provides more hearing aid gain and less distortion in the output sound quality than feedback cancelers with fixed gain functions

Optimal Step Size Technique for Frequency Domain and Partition Block Adaptive Filters for PEM based Acoustic Feedback Cancellation

The adaptive filtering approach has been commonly used to perform acoustic feedback cancellation (AFC) in digital hearing-aids due to its reliable performance and feasibility. Because the loudspeaker and microphone are close together in hearing aids, the corresponding signals are highly correlated, resulting in biased estimation if adaptive filters are used. This problem can be addressed with the help of the decorrelation prefilter by incorporating the Prediction Error Method (PEM) technique into AFC. Frequency-Domain Adaptive Filters (FDAF) are preferable over the time-domain implementation to achieve better performance in terms of convergence and computational complexity. In addition, Partition-Block Frequency-Domain Adaptive Filters (PBFDAF) offers low processing delay. However, because of their fixed step-size, there is a trade-off between initial convergence and steady-state misalignment in the widely used frequency-domain algorithms. While Variable Step-Size (VSS) algorithms can help with this issue, VSS techniques for frequency-domain algorithms have not been extensively studied in the context of PEM-AFC. Hence, in this paper, we presented an Optimal Step-Size (OSS) technique for both the FDAF-PEM_AFC and PBFDAF-PEM_AFC algorithms to simultaneously accomplish fast convergence and minimal steady-state error. A Feedback Path Change Detector (FPCD) was also incorporated into the proposed algorithms to address the problem of convergence in non-stationary feedback paths. The results of simulations show that the proposed algorithms are clearly superior, and they are encouraging

Noise injection for feedback cancellation with linear prediction

ABSTRACT Feedback oscillation is one of the major issues with hearing aids. An efficient way of feedback suppression is feedback cancellation, which uses an adaptive filter to estimate the feedback path. However, the feedback canceller suffers from the bias problem in the feedback path estimate. The recent progress suggests a feedback canceller with linear prediction of the desired signal in order to eliminate the bias when certain conditions are met. However, the bias still remains in many situations, for example when the input signal is voiced speech. Noise injection is investigated in this paper to help reduce the bias further and improve the system performance. Two nearly inaudible noises are proposed: a masking noise, which is tailored to the hearing-aid application, and a linear prediction based noise, which is especially efficient for feedback cancellation with linear prediction. Simulation results show that noise injection can further reduce the feedback estimation error by 1-4 dB and/or increase the stable gain by 3-4 dB, depending on the characteristics of the input signal