Acoustic feedback arises from the leakage of sound from the loudspeaker back to the microphone and is a common problem in hearing aids and public address systems. One popular solution to this problem is adaptive feedback cancellation (AFC) which utilizes an adaptive filter (AF) to estimate and subsequently subtract the feedback component from the microphone. Due to the closed-loop system the loudspeaker signal and the incoming signal exhibit a high correlation. However, model-based and more recently learning-based AF methods typically neglect this correlation in their derivation, leading to sub-optimal performance in closed-loop scenarios. In this paper, we propose Neural-AFC, a recurrent neural network (RNN) designed for AFC step-size control optimization, that addresses this problem by including not only the adaptive filter but also the acoustic feedback path and the system gain in the recurrence model. Our experiment results show that when trained within this closed-loop model, Neural-AFC improves the steady-state performance and (re)convergence rate compared to conventional and open-loop RNN-based AF in both hearing aid and public address system applications.

The hear-through functionality on hearing devices, which allows hearing equivalent to the open-ear while providing the possibility to modify the sound pressure at the eardrum in a desired manner, has drawn great attention from researchers in recent years. To this end, the output of the device is processed by means of an equalization filter, such that the transfer function between external sound sources and the eardrum is equivalent for the open-ear and the aided condition with the device in the ear. To achieve an ideal performance, the equalization filter design assumes the exact knowledge of all the relevant acoustic transfer functions. A particular challenge is the transfer function between the hearing device receiver and the eardrum, which is difficult to obtain in practice as it requires additional probe-tube measurements. In this work, we address this issue by proposing an individualized hear-through equalization filter design that leverages the measurement of the so-called secondary path to predict the sound pressure at the eardrum using a principle component analysis based estimator. Experimental results using real-ear measured transfer functions confirm that the proposed method achieves a good sound quality compared to the open-ear while outperforming filter designs that do not leverage the proposed estimator.

To improve the sound quality of hearing devices, equalization filters can be used to achieve acoustic transparency, i.e., listening with the device in the ear is perceptually similar to the open ear. The equalization filter needs to ensure that the superposition of the equalized signal played by the device and the signal leaking through the device into the ear canal matches a processed version of the signal reaching the eardrum of the open ear. Depending on the processing delay of the hearing device, comb-filtering artifacts can occur due to this superposition, which may degrade the perceived sound quality. In this paper, we propose a unified least-squares-based procedure to design single- and multi-loudspeaker equalization filters for hearing devices aimed at achieving acoustic transparency. To account for non-minimum phase components, we utilize a so-called group delay compensation. To reduce comb-filtering artifacts, we propose to use a frequency-dependent regularization. Experimental results using measured acoustic transfer functions from a multi-loudspeaker earpiece show that the proposed equalization filter design procedure results in robust acoustic transparency and reduces the impact of comb-filtering artifacts. A comparison between single- and multi-loudspeaker equalization shows that for both cases a robust equalization performance can be achieved for different desired open ear transfer functions.

Correspondence: henning.schepker@uni-oldenburg.de; simon.doclo@uni-oldenburg.de Signal Processing Group, Department of Medical Physics and Acoustics and Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany Full list of author information is available at the end of the article Currently with Starkey Hearing Technologies, Eden Prarie, Minnesota, United States.Currently with German Institute of Hearing Aids, Lübeck, Germany. Abstract

To improve sound quality in hearing devices, the hearing device output should be appropriately equalized. To achieve optimal individualized equalization typically requires knowledge of all transfer functions between the source, the hearing device, and the individual eardrum. However, in practice the measurement of all of these transfer functions is not feasible. This study investigates sound pressure equalization using different transfer function estimates. Specifically, an electro-acoustic model is used to predict the sound pressure at the individual eardrum, and average estimates are used to predict the remaining transfer functions. Experimental results show that using these assumptions a practically feasible and close-to-optimal individualized sound pressure equalization can be achieved.

Smart headphones or hearables use different types of algorithms such as noise cancelation, feedback suppression, and sound pressure equalization to eliminate undesired sound sources or to achieve acoustical transparency. Such signal processing strategies might alter the spectral composition or interaural differences of the original sound, which might be perceived by listeners as monaural or binaural distortions and thus degrade audio quality. To evaluate the perceptual impact of these distortions, subjective quality ratings can be used, but these are time consuming and costly. Auditory-inspired instrumental quality measures can be applied with less effort and may also be helpful in identifying whether the distortions impair the auditory representation of monaural or binaural cues. Therefore, the goals of this study were (a) to assess the applicability of various monaural and binaural audio quality models to distortions typically occurring in hearables and (b) to examine the effect of those distortions on the auditory representation of spectral, temporal, and binaural cues. Results showed that the signal processing algorithms considered in this study mainly impaired (monaural) spectral cues. Consequently, monaural audio quality models that capture spectral distortions achieved the best prediction performance. A recent audio quality model that predicts monaural and binaural aspects of quality was revised based on parts of the current data involving binaural audio quality aspects, leading to improved overall performance indicated by a mean Pearson linear correlation of 0.89 between obtained and predicted ratings.

Perception adapts to the properties of prior stimulation, as illustrated by phenomena such as visual color constancy or speech context effects. In the auditory domain, only little is known about adaptive processes when it comes to the attribute of auditory brightness. Here, we report an experiment that tests whether listeners adapt to spectral colorations imposed on naturalistic music and speech excerpts. Our results indicate consistent contrastive adaptation of auditory brightness judgments on a trial-by-trial basis. The pattern of results suggests that these effects tend to grow with an increase in the duration of the adaptor context but level off after around 8 trials of 2 s duration. A simple model of the response criterion yields a correlation of r = .97 with the measured data and corroborates the notion that brightness perception adapts on timescales that fall in the range of auditory short-term memory. Effects turn out to be similar for spectral filtering based on linear spectral filter slopes and filtering based on a measured transfer function from a commercially available hearing device. Overall, our findings demonstrate the adaptivity of auditory brightness perception under realistic acoustical conditions.

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.

Near-end listening enhancement (NELE) algorithms aim to preprocess speech prior to playback via loudspeakers so as to maintain high speech intelligibility even when listening conditions are not optimal, e.g., due to noise or reverberation. Often NELE algorithms are designed for scenarios considering either only the detrimental effect of noise or only reverberation, but not both disturbances. In many typical applications scenarios, however, both factors are present. In this paper, we evaluate a new combination of a noise-dependent and a reverberationdependent algorithm implemented in a common framework. Specifically, we use instrumental measures as well as subjective ratings of listening effort for acoustic scenarios with different reverberation times and realistic signal-to-noise ratios. The results show that the noise-dependent algorithm also performs well in reverberation, and that the combination of both algorithms can yield slightly better performance than the individual algorithms alone. This benefit appears to depend strongly on the specific acoustic condition, indicating that further work is required to optimize the adaptive algorithm behavior.

Acoustic feedback occurs in hearing aids due to the coupling between the hearing aid loudspeaker and microphone(s). In order to reduce the acoustic feedback, adaptive filters are commonly used to estimate the feedback contribution in the microphone(s). While theoretically allowing for perfect feedback cancellation, in practice the adaptive filter typically converges to a biased optimal solution due to the closed-loop acoustical system of the hearing aid. Previously it has therefore been proposed to suppress the acoustic feedback contribution for an earpiece with multiple integrated microphones and loudspeakers using a fixed null-steering beamformer and hence avoiding a biased adaption. While previous null-steering beamforming approaches aimed at perfect preservation of the incoming signal using its relative transfer function (RTF), in this article we propose to use a soft constraint that allows to trade off between incoming signal preservation and feedback suppression. We formulate the computation of the beamformer coefficients both as a least-squares optimization procedure, aiming to minimize the residual feedback power, and as a min-max optimization procedure, aiming to directly maximize the maximum stable gain of the hearing aid. Experimental evaluations were performed using measured acoustic feedback paths from a custom earpiece with two microphones in the vent and a third microphone in the concha. Results show that the proposed fixed null-steering beamformer using the RTF-based soft constraint provides a reduction of the acoustic feedback by 7-8 dB compared to the previously proposed RTF-based hard constraint while limiting the distortions of the incoming signal in the beamformer output.

Speech output is used extensively, including in situations where correct message reception is threatened by adverse listening conditions.Recently, there has been a growing interest in algorithmic modifications that aim to increase the intelligibility of both natural and synthetic speech when presented in noise.The Hurricane Challenge is the first large-scale open evaluation of algorithms designed to enhance speech intelligibility.Eighteen systems operating on a common data set were subjected to extensive listening tests and compared to unmodified natural and text-to-speech (TTS) baselines.The best-performing systems achieved gains over unmodified natural speech of 4.4 and 5.1 dB in competing speaker and stationary noise respectively, while TTS systems made gains of 5.6 and 5.1 dB over their baseline.Surprisingly, for most conditions the largest gains were observed for noise-independent algorithms, suggesting that performance in this task can be further improved by exploiting information in the masking signal.

In hearing devices, hear-through features that aim to provide the user with acoustic awareness of their surroundings are becoming increasingly popular. In particular, awareness of the user's surroundings can be achieved when the open ear properties can be perceptually restored with the device inserted, typically called acoustic transparency. In this study, we investigate the perceptual sound quality of six commercial consumer hearing devices and two research hearing devices with hear-through features. We conducted two experiments in which normal-hearing participants rated the perceptual sound quality of different audio signals processed by the hearing devices. In Experiment 1, the participants were not provided with an explicit open-ear reference, while in Experiment 2, the open-ear reference was explicitly provided. Results show that most commercial consumer hearing devices are not able to achieve a perceptual sound quality comparable to the open ear. Furthermore, results indicate that a main contributing factor to the overall quality of a hear-through feature is determined by the similarly of the transfer function with the device inserted and the open ear transfer function.

An increasing number of earphones and other hearing devices contain functionalities that are based on a so-called hear-through feature, which allows the user to hear the acoustic environment through the device. Ideally, the user would perceive the hear-through sound identical to listening with the open ear, which is referred to as acoustic transparency. In technical terms, this means that the sound transmission to the eardrum should be as similar as possible between the open ear and through the device. In this study, we evaluate the acoustic transparency of the hear-through feature of seven commercial hearables as well as two research hearing devices by means of technical measurements on a dummy head. A variety of artefacts, including frequency response deviations, comb filtering artefacts, and destruction of spatial cues, were revealed and quantified, and surprisingly large differences between current devices are noted. The corresponding subjective sound quality has been assessed in a companion study.

Speech playback (e.g., TV, radio, public address) becomes harder to understand in the presence of noise and reverberation. NELE (Near End Listening Enhancement) algorithms can improve intelligibility by modifying the signal before it is played back. Substantial intelligibility improvements have been achieved in the lab for both natural and synthetic speech. However, evidence is still scarce on how these algorithms work under conditions of realistic noise and reverberation. We present a realistic test platform, featuring two representative everyday scenarios in which speech playback may occur (in the presence of both noise and reverberation): a domestic space (living room) and a public space (cafeteria). The generated stimuli are evaluated by measuring keyword accuracy rates in a listening test with normal hearing subjects. We use the new platform to compare three state-of-the-art NELE algorithms, employing either noise-adaptive or non-adaptive strategies, and with or without compensation for reverberation.

In this paper we evaluate the performance of a real-time hearing device prototype that aims at achieving acoustically transparent sound presentation. Acoustic transparency refers to the perceptual equivalence of the sound at the aided ear drum, i.e., with the hearing device inserted and processing on, and the open ear drum, i.e., without the hearing device inserted. The considered hearing device combines a custom earpiece with multiple microphones and signal processing algorithms for robust feedback suppression and sound pressure equalization. We evaluate the perceived overall sound quality of this prototype using dummy head recordings in different acoustic conditions using a multi-stimulus with hidden reference and anchor-like framework with N = 15 normal-hearing subjects. Results show that the overall sound quality can be significantly improved for all conditions by using sound pressure equalization, where the processing delay of the device is a crucial limiting factor of the sound quality.

Speech playback (e.g., TV, radio, public address) becomes harder to understand in the presence of noise and reverberation. NELE (Near End Listening Enhancement) algorithms can improve intelligibility by modifying the signal before it is played back. Substantial intelligibility improvements have been achieved in the lab for both natural and synthetic speech. However, evidence is still scarce on how these algorithms work under conditions of realistic noise and reverberation. We present a realistic test platform, featuring two representative everyday scenarios in which speech playback may occur (in the presence of both noise and reverberation): a domestic space (living room) and a public space (cafeteria). The generated stimuli are evaluated by measuring keyword accuracy rates in a listening test with normal hearing subjects. We use the new platform to compare three state-of-theart NELE algorithms, employing either noise-adaptive or nonadaptive strategies, and with or without compensation for reverberation.

Earpieces that include one or more microphones and drivers are required in many research applications related to hearing devices, however suitable devices are often not readily available. In this contribution, we present the development and evaluation of an earpiece for research on assistive hearing devices and hearables. The earpiece includes two balanced armature drivers as well as four microphones, which are built into a one-size-fits-all acrylic shell. It features custom transducer positioning at different positions inside a vent, as well as a microphone inside the ear canal. We discuss details on the earpiece design, present acoustic measurements and discuss the eligibility for different applications. The earpiece is openly available both in a vented as well as an occluded version.

In hearing aids, acoustic feedback occurs due to the coupling between the hearing aid loudspeaker and microphone(s). In order to reduce the acoustic feedback, adaptive filters are commonly used to estimate the feedback contribution in the microphone(s). While theoretically allowing for perfect feedback cancellation, in practice the adaptive filter converges to an optimal solution that is typically biased due to the closed-loop acoustical system of the hearing aid. In order to avoid the adaptation to a biased optimal solution, in this paper we propose to use a fixed beamformer to cancel the acoustic feedback contribution for an earpiece with multiple integrated microphones and loudspeakers. By steering a spatial null in the direction of the hearing aid loudspeaker, we show that theoretically perfect feedback cancellation can be achieved. While previous null-steering beamforming approaches did not control for distortions of the incoming signal, in this paper we propose to incorporate a constraint based on the relative transfer function (RTF) of the incoming signal, aiming to perfectly preserve this signal. We formulate the computation of the beamformer coefficients both as a least-squares optimization procedure, aiming to minimize the residual feedback power, and as a min–max optimization procedure, aiming to directly maximize the maximum stable gain of the hearing aid. Experimental results using measured acoustic feedback paths from a custom earpiece with two microphones in the vent and a third microphone in the concha show that the proposed fixed null-steering beamformer using the RTF-based constraint provides a reduction of the acoustic feedback and substantially increases the added stable gain while preserving the incoming signal. This can even be achieved for unknown acoustic feedback paths and incoming signal directions.

In hearing devices, acoustic feedback frequently occurs due to the coupling between the hearing device loudspeaker(s) and microphone(s). In order to remove the feedback component from the microphone(s), adaptive filters are commonly used. While many hearing devices contain only a single loudspeaker, in this paper we consider a hearing device with multiple loudspeakers in the vent of a custom earpiece. We exploit this availability by pre-processing the loudspeaker signals such that they interfere destructively at the hearing device microphone while the signal at the eardrum is preserved. More specifically, we design a spatial pre-processor that aims at maximizing the maximum stable gain while limiting the distortions of the desired signal at the eardrum. Experimental results using measured impulse responses from a custom hearing device with two loudspeakers show that the proposed approach yields a robust reduction of the acoustic feedback while preserving the desired signal at the eardrum.

Adaptive feedback cancellation (AFC) techniques are common in modern hearing aid devices (HADs) since these techniques have been successful in increasing the stable gain. Accordingly, there has been a significant effort to improve AFC technology, especially for open-fitting and in-ear HADs, for which howling is more prevalent due to the large acoustic coupling between the loudspeaker and the microphone. In this paper, the authors propose a hybrid AFC (H-AFC) scheme that is able to shorten the time it takes to recover from howling. The proposed H-AFC scheme consists of a switched combination adaptive filter, which is controlled by a soft-clipping-based stability detector to select either the standard normalized least mean squares (NLMS) algorithm or the prediction-error-method (PEM) NLMS algorithm to update the adaptive filter. The standard NLMS algorithm is used to obtain fast convergence, while the PEM-NLMS algorithm is used to provide a low bias solution. This stability-controlled adaptation is hence the means to improve performance in terms of both convergence rate as well as misalignment, while only slightly increasing computational complexity. The proposed H-AFC scheme has been evaluated for both speech and music signals, resulting in a significantly improved convergence and re-convergence rate, i.e., a shorter howling period, as well as a lower average misalignment and a larger added stable gain compared to using either the NLMS or the PEM-NLMS algorithm alone. An objective evaluation using the perceptual evaluation of speech quality and the perceptual evaluation of audio quality measures shows that the proposed H-AFC scheme provides very high-quality speech and music signals. This has also been verified through a subjective listening experiment with N = 15 normal-hearing subjects using a multi-stimulus test with hidden reference and anchor, showing that the proposed H-AFC scheme results in a better perceptual quality than the state-of-the-art PEM-NLMS algorithm.