Hearing Aid Algorithms Research Articles

Localization of virtual sound sources with bilateral hearing aids in realistic acoustical scenes

Sound localization with hearing aids has traditionally been investigated in artificial laboratory settings. These settings are not representative of environments in which hearing aids are used. With individual Head-Related Transfer Functions (HRTFs) and room simulations, realistic environments can be reproduced and the performance of hearing aid algorithms can be evaluated. In this study, four different environments with background noise have been implemented in which listeners had to localize different sound sources. The HRTFs were measured inside the ear canals of the test subjects and by the microphones of Behind-The-Ear (BTEs) hearing aids. In the first experiment the system for virtual acoustics was evaluated by comparing perceptual sound localization results for the four scenes in a real room with a simulated one. In the second experiment, sound localization with three BTE algorithms, an omnidirectional microphone, a monaural cardioid-shaped beamformer and a monaural noise canceler, was examined. The results showed that the system for generating virtual environments is a reliable tool to evaluate sound localization with hearing aids. With BTE hearing aids localization performance decreased and the number of front-back confusions was at chance level. The beamformer, due to its directivity characteristics, allowed the listener to resolve the front-back ambiguity.

Improving Hearing Aid Function in Noisy Situations

Improving Hearing Aid Function in Noisy Situations

Microphone Directionality, Pre-Emphasis Filter, and Wind Noise in Cochlear Implants

Wind noise can be a nuisance or a debilitating masker for cochlear implant users in outdoor environments. Previous studies indicated that wind noise at the microphone/hearing aid output had high levels of low-frequency energy and the amount of noise generated is related to the microphone directionality. Currently, cochlear implants only offer either directional microphones or omnidirectional microphones for users at-large. As all cochlear implants utilize pre-emphasis filters to reduce low-frequency energy before the signal is encoded, effective wind noise reduction algorithms for hearing aids might not be applicable for cochlear implants. The purposes of this study were to investigate the effect of microphone directionality on speech recognition and perceived sound quality of cochlear implant users in wind noise and to derive effective wind noise reduction strategies for cochlear implants. A repeated-measure design was used to examine the effects of spectral and temporal masking created by wind noise recorded through directional and omnidirectional microphones and the effects of pre-emphasis filters on cochlear implant performance. A digital hearing aid was programmed to have linear amplification and relatively flat in-situ frequency responses for the directional and omnidirectional modes. The hearing aid output was then recorded from 0 to 360° at flow velocities of 4.5 and 13.5 m/sec in a quiet wind tunnel. Sixteen postlingually deafened adult cochlear implant listeners who reported to be able to communicate on the phone with friends and family without text messages participated in the study. Cochlear implant users listened to speech in wind noise recorded at locations that the directional and omnidirectional microphones yielded the lowest noise levels. Cochlear implant listeners repeated the sentences and rated the sound quality of the testing materials. Spectral and temporal characteristics of flow noise, as well as speech and/or noise characteristics before and after the pre-emphasis filter, were analyzed. Correlation coefficients between speech recognition scores and crest factors of wind noise before and after pre-emphasis filtering were also calculated. Listeners obtained higher scores using the omnidirectional than the directional microphone mode at 13.5 m/sec, but they obtained similar speech recognition scores for the two microphone modes at 4.5 m/sec. Higher correlation coefficients were obtained between speech recognition scores and crest factors of wind noise after pre-emphasis filtering rather than before filtering. Cochlear implant users would benefit from both directional and omnidirectional microphones to reduce far-field background noise and near-field wind noise. Automatic microphone switching algorithms can be more effective if the incoming signal were analyzed after pre-emphasis filters for microphone switching decisions.

Enhancing the energy efficiency of wireless-communicated binaural hearing aids for speech separation driven by soft-computing algorithms

AbstractAssisted by soft computing methods, the work we present in this paper focuses on the design of energy-efficient algorithms for binaural hearing aids that aim to separate speech from other sounds the hearing impaired person is not interested in. To do this, the right and left hearing aids need to wirelessly transmit to each other some parameters involved in the speech separation algorithm. The problem is that this transmission appreciably reduces the battery life, the most important constrain for designing advanced algorithms in hearing aids. Reducing the number of bits used to represent the parameters to be transmitted will bring down the power consumption, but at the expense of degrading the ability of the system to separate the speech from the other sound sources. Aiming at solving this problem, our approach, based on quantizing the parameters to be transmitted, basically consists in computing the adequate number of quantization bits by means of a combination of neural networks and genetic algorithms in the effort of finding a balance between low bit rate (and thus, low power consumption) and good separation of speech. The results show that even by using only 2bits/quantized-sample, the quality of the separation is as high as 70% of the limiting non-quantized quality separation factor, which has been found to be 85%.

Hearing Apparatus And A Method For Own-Voice Detection

A hearing aid wearer's own voice frequently leads to artifacts and response errors in various hearing aid algorithms. It is provided that the user's own voice to be detected by a special analysis device, and the hearing aid algorithms can be controlled as a function of detection. This can be achieved by providing a microphone in the auditory channel whose signal level is compared with that of an external microphone. This allows some form of control, e.g., the automatic gain control of a hearing aid to be “frozen”, in the presence of the hearing aid wearer's own voice.

Sensory-cognitive interaction in the neural encoding of speech in noise: a review.

Speech-in-noise (SIN) perception is one of the most complex tasks faced by listeners on a daily basis. Although listening in noise presents challenges for all listeners, background noise inordinately affects speech perception in older adults and in children with learning disabilities. Hearing thresholds are an important factor in SIN perception, but they are not the only factor. For successful comprehension, the listener must perceive and attend to relevant speech features, such as the pitch, timing, and timbre of the target speaker's voice. Here, we review recent studies linking SIN and brainstem processing of speech sounds. To review recent work that has examined the ability of the auditory brainstem response to complex sounds (cABR), which reflects the nervous system's transcription of pitch, timing, and timbre, to be used as an objective neural index for hearing-in-noise abilities. We examined speech-evoked brainstem responses in a variety of populations, including children who are typically developing, children with language-based learning impairment, young adults, older adults, and auditory experts (i.e., musicians). In a number of studies, we recorded brainstem responses in quiet and babble noise conditions to the speech syllable /da/ in all age groups, as well as in a variable condition in children in which /da/ was presented in the context of seven other speech sounds. We also measured speech-in-noise perception using the Hearing-in-Noise Test (HINT) and the Quick Speech-in-Noise Test (QuickSIN). Children and adults with poor SIN perception have deficits in the subcortical spectrotemporal representation of speech, including low-frequency spectral magnitudes and the timing of transient response peaks. Furthermore, auditory expertise, as engendered by musical training, provides both behavioral and neural advantages for processing speech in noise. These results have implications for future assessment and management strategies for young and old populations whose primary complaint is difficulty hearing in background noise. The cABR provides a clinically applicable metric for objective assessment of individuals with SIN deficits, for determination of the biologic nature of disorders affecting SIN perception, for evaluation of appropriate hearing aid algorithms, and for monitoring the efficacy of auditory remediation and training.

Noise-induced hearing loss alters the temporal dynamics of auditory-nerve responses

Auditory-nerve fibers demonstrate dynamic response properties in that they adapt to rapid changes in sound level, both at the onset and offset of a sound. These dynamic response properties affect temporal coding of stimulus modulations that are perceptually relevant for many sounds such as speech and music. Temporal dynamics have been well characterized in auditory-nerve fibers from normal-hearing animals, but little is known about the effects of sensorineural hearing loss on these dynamics. This study examined the effects of noise-induced hearing loss on the temporal dynamics in auditory-nerve fiber responses from anesthetized chinchillas. Post-stimulus-time histograms were computed from responses to 50-ms tones presented at characteristic frequency and 30 dB above fiber threshold. Several response metrics related to temporal dynamics were computed from post-stimulus-time histograms and were compared between normal-hearing and noise-exposed animals. Results indicate that noise-exposed auditory-nerve fibers show significantly reduced response latency, increased onset response and percent adaptation, faster adaptation after onset, and slower recovery after offset. The decrease in response latency only occurred in noise-exposed fibers with significantly reduced frequency selectivity. These changes in temporal dynamics have important implications for temporal envelope coding in hearing-impaired ears, as well as for the design of dynamic compression algorithms for hearing aids.

Multicenter evaluation of signal enhancement algorithms for hearing aids

In the framework of the European HearCom project, promising signal enhancement algorithms were developed and evaluated for future use in hearing instruments. To assess the algorithms' performance, five of the algorithms were selected and implemented on a common real-time hardware/software platform. Four test centers in Belgium, The Netherlands, Germany, and Switzerland perceptually evaluated the algorithms. Listening tests were performed with large numbers of normal-hearing and hearing-impaired subjects. Three perceptual measures were used: speech reception threshold (SRT), listening effort scaling, and preference rating. Tests were carried out in two types of rooms. Speech was presented in multitalker babble arriving from one or three loudspeakers. In a pseudo-diffuse noise scenario, only one algorithm, the spatially preprocessed speech-distortion-weighted multi-channel Wiener filtering, provided a SRT improvement relative to the unprocessed condition. Despite the general lack of improvement in SRT, some algorithms were preferred over the unprocessed condition at all tested signal-to-noise ratios (SNRs). These effects were found across different subject groups and test sites. The listening effort scores were less consistent over test sites. For the algorithms that did not affect speech intelligibility, a reduction in listening effort was observed at 0 dB SNR.

Assessing the benefit of adaptive null-steering using real-world signals

This study compared the noise reduction of adaptive null-steering and near-hypercardioid directional hearing-aid algorithms via performance on real-world signals. Using subject-individualized and generic (i.e. similar to current hearing aids), off-line frequency-domain implementations, we processed recordings made through two microphones of a BTE device worn by five subjects. Recording scenarios included homes, offices, cafés, streets, buses, and automobiles. We found practically all (> 95% of recording time) adaptive noise-reduction benefit for generic implementations is below 1.2 dB, and 96% and 92% is below 2 dB for 16-and 32-band individualized implementations, respectively. A 256-band, individualized implementation showed a majority of benefit between 1–4 dB. We found no extended (> 2 s) continuous periods of significant (> 2 dB) benefit for the generic adaptive implementations. The recordings—having many independent and simultaneously active sources, spatially extended sources, significant reverberation, or combinations thereof—indicate an environment comprising few instances of high direct-to-diffuse energy situations. Combined with results from previous field trials, the evidence suggests that such an environment is common and represents a significant limitation on adaptive benefit.SumarioEste estudio compara la reducción del ruido en algoritmos de auxiliares auditivos con direccionamento anulado o direccionamiento casi-hipercardioides por medio del rendimiento con señales de mundo real. Usando implementaciones fuera de línea en campos frecuenciales, individualizadas y genéricas (p.ej. similares a las de los auxiliares auditivos usuales), procesamos los registros realizados con dos micrófonos de un instrumento BTE usado por cinco sujetos. Los escenarios de registro incluyeron hogares, oficinas, cafés, calles, autobuses y automóviles. Encontramos prácticamente en todas las implementaciones (> 95% del tiempo de registro) que el beneficio en la reducción de ruido adaptable es inferior a 1.2 dB y que en 96% y 92% es menor a 2 dB para implementaciones individualizadas en bandas de 16 y 32, respectivamente. Una implementación individualizada en una banda de 256, mostró la mayoría de los beneficios entre 1-4 dB. No encontramos períodos continuos extendidos (> 2 s) de beneficio significativo (> 2 dB) con las implementaciones genéricas adaptables. Los registros –que tienen muchas fuentes independientes o simultáneamente activas, extendidas espacialmente, con reverberación significativa o combinaciones de los mismos- indican un ambiente que comprende pocas instancias de situaciones de alta energía de difusión directa. La evidencia, combinada con los resultados de pruebas libres previas, sugiere que este ambiente es común y representa una limitación significativa para el beneficio de adaptación.

Database of Multichannel In-Ear and Behind-the-Ear Head-Related and Binaural Room Impulse Responses

An eight-channel database of head-related impulse responses (HRIRs) and binaural room impulse responses (BRIRs) is introduced. The impulse responses (IRs) were measured with three-channel behind-the-ear (BTEs) hearing aids and an in-ear microphone at both ears of a human head and torso simulator. The database aims at providing a tool for the evaluation of multichannel hearing aid algorithms in hearing aid research. In addition to the HRIRs derived from measurements in an anechoic chamber, sets of BRIRs for multiple, realistic head and sound-source positions in four natural environments reflecting daily-life communication situations with different reverberation times are provided. For comparison, analytically derived IRs for a rigid acoustic sphere were computed at the multichannel microphone positions of the BTEs and differences to real HRIRs were examined. The scenes' natural acoustic background was also recorded in each of the real-world environments for all eight channels. Overall, the present database allows for a realistic construction of simulated sound fields for hearing instrument research and, consequently, for a realistic evaluation of hearing instrument algorithms.

Reduced-Bandwidth and Distributed MWF-Based Noise Reduction Algorithms for Binaural Hearing Aids

In a binaural hearing aid system, output signals need to be generated for the left and the right ear. Using the binaural multichannel Wiener filter (MWF), which exploits all microphone signals from both hearing aids, a significant reduction of background noise can be achieved. However, due to power and bandwidth limitations of the binaural link, it is typically not possible to transmit all microphone signals between the hearing aids. To limit the amount of transmitted information, this paper presents reduced-bandwidth MWF-based noise reduction algorithms, where a filtered combination of the contralateral microphone signals is transmitted. A first scheme uses a signal-independent beamformer, whereas a second scheme uses the output of a monaural MWF on the contralateral microphone signals and a third scheme involves an iterative distributed MWF (DB-MWF) procedure. It is shown that in the case of a rank-1 speech correlation matrix, corresponding to a single speech source, the DB-MWF procedure converges to the binaural MWF solution. Experimental results compare the noise reduction performance of the reduced-bandwidth algorithms with respect to the benchmark binaural MWF. It is shown that the best performance of the reduced-bandwidth algorithms is obtained by the DB-MWF procedure and that the performance of the DB-MWF procedure approaches quite well the optimal performance of the binaural MWF.

The use of virtual acoustics in the evaluation and development of binaural hearing aid algorithms

The development of noise reduction algorithms for hearing aids (HA) is not longer only related to the improvement of signal to noise ratio, but also to the quality of hearing, e.g., binaural aspects of hearing. This is very important for the recognition of the localization of sound sources but also for an improved speech intelligibility in noisy situations due to spatial release from masking effects. New design and signal processing algorithms for binaural HA's need to be tested and validated in different acoustical scenarios. As it is too laborious and time consuming to perform sufficient numbers of perceptual evaluations in different rooms with different acoustical parameters, advanced acoustic modeling of different virtual acoustical environments might be needed. Virtual acoustics in our research relates to the convolution of the measured or simulated binaural signals (head related transfer functions - HRTF's) with the impulse response generated from a computer model of a room (using ODEON® software) to simulate binaural sounds. This study investigates the usage of virtual acoustics in the framework of developing algorithms for binaural hearing aids. It evaluates and quantifies the fidelity of binaural signals generated by commercially available virtual acoustics software with respect to the localization of sound and speech intelligibility in different acoustical scenarios.

Prediction of perceived sound quality of hearing aids (algorithms) using perceptual models

This contribution presents an overview of basic approaches for predicting the perceived sound quality of hearing aids and hearing aid algorithms using auditory processing models for hearing impaired. Comparison‐based concepts will be considered in particular. The main characteristic of these concepts is the comparison of internal representations (the outputs of the auditory models) of a test and a reference signal. While this approach is straight‐forward and has proven to be successful for the prediction of sound quality of lossy speech and audio processing systems perceived by normal‐hearing listeners, the requirement of a reference representing the optimal quality can pose a problem in the case of hearing aids and hearing‐impaired listeners. The potential and limitations of comparison‐based approaches will be illustrated by example results from different studies obtained with an extended version of the perceptual audio quality model PEMO‐Q.

Model-based objective assessment of noise reduction systems for hearing aids

Since the ultimative goal of hearing-aid development is the (subjective) judgment of the individual hearing-impaired listener, time-consuming tests with the end user are indispensable. However, time- and effort-saving objective methods to assess the potential benefit of different versions and parameter sets of hearing aid algorithms are gaining importance. This contribution reviews perception-model-based approaches to predict the hearing-impaired judgement and speech reception performance achieved with various noise reduction schemes. The perceptual similarity measure PSM evaluates the similarity between a tested condition and an "ideal" reference condition not on the physical level, but rather on the perceptual level at the output of a perception model for the individual hearing-impaired listener. The binaural extention of the SII approach uses a binaural preprocessing stage followed by a speech intelligibility index (SII)- based prediction scheme capable of predicting the relative benefit of binaural signal presentation and signal enhancement in complex spatial signal and noise source configurations. Both model-based schemes can be combined to assess the effect of noise reduction algorithms (such as adaptive beamformers) and to optimize their respective performance for different acoustical situations.

Binaural Noise Reduction Algorithms for Hearing Aids That Preserve Interaural Time Delay Cues

Binaural hearing aids use microphone inputs from both the left and right hearing aid to generate an output for each ear. On the other hand, a monaural hearing aid generates an output by processing only its own microphone inputs. This correspondence presents a binaural extension of a monaural multichannel noise reduction algorithm for hearing aids based on Wiener filtering. In addition to significantly suppressing the noise interference, the algorithm preserves the interaural time delay (ITD) cues of the speech component, thus allowing the user to correctly localize the speech source. Unfortunately, binaural multichannel Wiener filtering distorts the ITD cues of the noise source. By adding a parameter to the cost function the amount of noise reduction performed by the algorithm can be controlled, and traded off for the preservation of the noise ITD cues

Interactive fitting of multiple algorithms implemented in the same digital hearing aid

The main purpose of this study is to examine the applicability of an adaptive and interactive optimization strategy to fine-tune three hearing-aid algorithms simultaneously: dynamic compression, temporal signal enhancement, and noise reduction. The optimal combination of these three algorithms was determined by a multidirectional pattern search with an adaptive step size. Additionally, we applied a round-robin procedure to validate the results of the optimization procedure. For both procedures the listeners were asked to compare two consecutive, differently processed sentences in continuous and fluctuating background noises on speech intelligibility. Ten hearing-impaired and four normal-hearing subjects participated. The reliability and consistency of the multidirectional pattern search was low, especially for the fluctuating noise condition. The results of the round-robin procedure did not correspond closely with the pattern search results. These findings suggest that the current implementation of a multidirectional optimization procedure has not yet proven to be applicable for the necessary individual fine tuning of complex signal processing strategies, when the objective is to maximize speech intelligibility.

Perception-model-based sound quality prediction for hearing aids: Big effect, small effect, and the optimization of noise reduction algorithms

Objective sound quality models can be used to optimize hearing aid algorithms and their respective parameter settings by numerically predicting the subjective sound quality assessed by humans without having to perform quality measurements with humans concurrently. The big effect prediction considers how the (individual) hearing loss affects the aided or unaided perceived sound quality. The small effect prediction aims at subtle differences at a given amplification level of the hearing aid in order to compare processing parameters. This talk will concentrate on benchmarking different types of objective prediction methods both for big effect and small effect conditions using a wide range of signals and processing conditions, respectively. In addition, the performance of PEMO—Q, an auditory-model-based quality prediction scheme developed at Oldenburg University will be discussed, which has been modified to include the effect of hearing impairment and the adaptation to different kinds of test data sets. We will also demonstrate the degree to which PEMO—Q is beneficial for optimizing noise reduction algorithms that are suitable for hearing aids. The objective measure helps to efficiently scan the multidimensional parameter space for an optimum parameter set of the noise reduction algorithm. [Work supported by BMBF and by HoerTech.]

Comparison of adaptive noise reduction algorithms in dual microphone hearing aids

In this paper, a physical and perceptual evaluation of two adaptive noise reduction algorithms for dual-microphone hearing aids are described. This is the first comparison between a fixed directional microphone on the one hand, and an adaptive directional microphone and an adaptive beamformer on the other hand, all implemented in the same digital hearing aid. The adaptive directional microphone is state-of-the-art in most modern commercial hearing aids. The physical evaluation shows the importance of an individual calibration procedure for the performance of the noise reduction algorithms with two microphone hearing aids. The directivity index calculated in anechoic conditions and intelligibility-weighted polar diagrams measured in reverberant conditions show that all the noise reduction strategies yield an improved signal-to-noise ratio (SNR), but that the adaptive beamformer generally performs best. From the perceptual evaluation, it is demonstrated that the adaptive beamformer always performs best in single noise source scenarios. In a more complex noise scenario, there is still a SNR improvement with all the techniques, however the effect is the same for all the strategies.

Methodological aspects of an adaptive multidirectional pattern search to optimize speech perception using three hearing-aid algorithms

In this study we investigated the reliability and convergence characteristics of an adaptive multidirectional pattern search procedure, relative to a nonadaptive multidirectional pattern search procedure. The procedure was designed to optimize three speech-processing strategies. These comprise noise reduction, spectral enhancement, and spectral lift. The search is based on a paired-comparison paradigm, in which subjects evaluated the listening comfort of speech-in-noise fragments. The procedural and nonprocedural factors that influence the reliability and convergence of the procedure are studied using various test conditions. The test conditions combine different tests, initial settings, background noise types, and step size configurations. Seven normal hearing subjects participated in this study. The results indicate that the reliability of the optimization strategy may benefit from the use of an adaptive step size. Decreasing the step size increases accuracy, while increasing the step size can be beneficial to create clear perceptual differences in the comparisons. The reliability also depends on starting point, stop criterion, step size constraints, background noise, algorithms used, as well as the presence of drifting cues and suboptimal settings. There appears to be a trade-off between reliability and convergence, i.e., when the step size is enlarged the reliability improves, but the convergence deteriorates.

Development of a flexible, realistic hearing in noise test environment (R-HINT-E)

Through the use of DSP chips and multiple microphones, hearing aids now offer the possibility of performing signal-to-noise enhancement. Evaluating different algorithms before they are instantiated on a hearing aid is essential. However, commercially available tests of hearing in noise do not allow for speech perception evaluation with a variety of signals, noise types, signal and noise locations, and reverberation. Here we present a flexible realistic hearing in noise testing environment (R-HINT-E) that involves (1) measuring the impulse responses at microphones placed in the ears of a human head and torso model (KEMAR) from many different locations in real rooms of various dimensions and with various reverberation characteristics, (2) creating a corpus of sentences based on the hearing in noise test recorded in quiet from a variety of talkers, (3) creating “soundscapes” representing the input to the ears (or array of microphones in a hearing aid) by convolving specific sentences or noises with the impulse responses for specific locations in a room, and (4) using psychophysical procedures for measuring reception thresholds for speech under a variety of noise conditions. Preliminary evaluation based on the engineering signal-to-error ratio and on human perceptual tests indicates that the convolved sounds closely match real recordings from the same location in the room. R-HINT-E should be invaluable for the evaluation of hearing aid algorithms, as well as more general signal separation algorithms such as independent components analysis.