Noise Reduction In Hearing Aids Research Articles

Deep learning-based auditory attention decoding in listeners with hearing impairment * *This paper’s data analysis is based on a Master’s Thesis work [].

Objective. This study develops a deep learning (DL) method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hearing impairment (HI). It addresses three classification tasks: differentiating noise from speech-in-noise, classifying the direction of attended speech (left vs. right) and identifying the activation status of hearing aid noise reduction algorithms (OFF vs. ON). These tasks contribute to our understanding of how hearing technology influences auditory processing in the hearing-impaired population. Approach. Deep convolutional neural network (DCNN) models were designed for each task. Two training strategies were employed to clarify the impact of data splitting on AAD tasks: inter-trial, where the testing set used classification windows from trials that the training set had not seen, and intra-trial, where the testing set used unseen classification windows from trials where other segments were seen during training. The models were evaluated on EEG data from 31 participants with HI, listening to competing talkers amidst background noise. Main results. Using 1 s classification windows, DCNN models achieve accuracy (ACC) of 69.8%, 73.3% and 82.9% and area-under-curve (AUC) of 77.2%, 80.6% and 92.1% for the three tasks respectively on inter-trial strategy. In the intra-trial strategy, they achieved ACC of 87.9%, 80.1% and 97.5%, along with AUC of 94.6%, 89.1%, and 99.8%. Our DCNN models show good performance on short 1 s EEG samples, making them suitable for real-world applications. Conclusion: Our DCNN models successfully addressed three tasks with short 1 s EEG windows from participants with HI, showcasing their potential. While the inter-trial strategy demonstrated promise for assessing AAD, the intra-trial approach yielded inflated results, underscoring the important role of proper data splitting in EEG-based AAD tasks. Significance. Our findings showcase the promising potential of EEG-based tools for assessing auditory attention in clinical contexts and advancing hearing technology, while also promoting further exploration of alternative DL architectures and their potential constraints.

Effect of Digital Noise Reduction in Hearing Aids on Speech Intelligibility in Both Quiet and Noisy Environments.

Digital noise reduction (DNR) minimizes the effect of noise on speech signals by continuously monitoring frequency bands in the presence of noise. In the present study, we explored the effect of DNR technology on speech intelligibility in individuals using hearing aids (HAs) and investigated implications for daily use. Eighteen participants with bilateral moderate sensorineural hearing loss (aged 16-45 years) were included. Bilateral receiver-in-the-ear HAs were fitted in the participants. The adaptive and nonadaptive (with a signal-to-noise ratio (SNR) of +5 and -5 dB, respectively) Turkish matrix sentence test (TURMatrix) in noise and free-field hearing assessments, including hearing thresholds with hearing aids, speech recognition thresholds (SRT), and speech discrimination scores, were conducted in two different conditions: HA in the DNR-on and DNR-off conditions. No significant difference was observed between free-field hearing assessments with the HA in the DNR-off and DNR-on conditions (P > 0.05). Furthermore, the adaptive and nonadaptive TURMatrix revealed significant differences between the scores under the DNR-on and DNR-off conditions (P < 0.05). Nevertheless, under the DNR-on condition, there was no correlation between free-field hearing assessments with HA and TURMatrix results (P > 0.05). However, a significant correlation was observed between SRT scores with HA and TURMatrix scores (adaptive and nonadaptive, +5 and -5 dB SNR, respectively) under the DNR-off condition (P < 0.05). Our study findings suggest that DNR can improve speech intelligibility in noisy environments. Therefore, DNR can enhance an individual's auditory comfort by improving their capacity to grasp speech in background noise.

Evaluation of impulse noise reduction in hearing aids with technical measurements and ratings of discomfort

Short, impulse-like sounds such as slamming of a door or rattle of dishes can be uncomfortable for hearing aid users. Therefore, many hearing aids provide impulse (or transient) noise reduction (INR) that should reduce loud and short sounds without impairing desired signals. In this work, we want to address the question whether hearing aid users require this type of signal processing to experience impulse sounds similarly as normal-hearing listeners. For this purpose, we evaluated INR in six commercially available hearing aids with technical measurements and with test subjects. During the technical evaluation, we presented seven different impulse signals to the hearing aids attached to a head and torso simulator (HATS) and determined the C-weighted peak sound pressure levels (LC,peak) at the output in different configurations. For the evaluation with test subjects, the discomfort of the same impulse sounds was rated by 24 hearing-impaired and 20 normal-hearing subjects. All subjects rated the discomfort unaided, and the hearing-impaired subjects also while successively wearing all six hearing aids with and without activated INR. As a main conclusion, hearing aid users without INR did not experience more discomfort compared to normal-hearing listeners for most of the impulse signals tested including the most uncomfortable ones, but INR further reduced experienced discomfort. Moreover, the technical measurements were correlated with the subjective ratings on discomfort.

Speech to noise ratio improvement induces nonlinear parietal phase synchrony in hearing aid users.

ObjectivesComprehension of speech in adverse listening conditions is challenging for hearing-impaired (HI) individuals. Noise reduction (NR) schemes in hearing aids (HAs) have demonstrated the capability to help HI to overcome these challenges. The objective of this study was to investigate the effect of NR processing (inactive, where the NR feature was switched off, vs. active, where the NR feature was switched on) on correlates of listening effort across two different background noise levels [+3 dB signal-to-noise ratio (SNR) and +8 dB SNR] by using a phase synchrony analysis of electroencephalogram (EEG) signals.DesignThe EEG was recorded while 22 HI participants fitted with HAs performed a continuous speech in noise (SiN) task in the presence of background noise and a competing talker. The phase synchrony within eight regions of interest (ROIs) and four conventional EEG bands was computed by using a multivariate phase synchrony measure.ResultsThe results demonstrated that the activation of NR in HAs affects the EEG phase synchrony in the parietal ROI at low SNR differently than that at high SNR. The relationship between conditions of the listening task and phase synchrony in the parietal ROI was nonlinear.ConclusionWe showed that the activation of NR schemes in HAs can non-linearly reduce correlates of listening effort as estimated by EEG-based phase synchrony. We contend that investigation of the phase synchrony within ROIs can reflect the effects of HAs in HI individuals in ecological listening conditions.

A Glimpse of Memory Through the Eyes: Pupillary Responses Measured During Encoding Reflect the Likelihood of Subsequent Memory Recall in an Auditory Free Recall Test.

The aim of the current study was to investigate whether task-evoked pupillary responses measured during encoding, individual working memory capacity and noise reduction in hearing aids were associated with the likelihood of subsequently recalling an item in an auditory free recall test combined with pupillometry. Participants with mild to moderately severe symmetrical sensorineural hearing loss (n = 21) were included. The Sentence-final Word Identification and Recall (SWIR) test was administered in a background noise composed of sixteen talkers with noise reduction in hearing aids activated and deactivated. The task-evoked peak pupil dilation (PPD) was measured. The Reading Span (RS) test was used as a measure of individual working memory capacity. Larger PPD at a single trial level was significantly associated with higher likelihood of subsequently recalling a word, presumably reflecting the intensity of attention devoted during encoding. There was no clear evidence of a significant relationship between working memory capacity and subsequent memory recall, which may be attributed to the SWIR test and RS test being administered in different modalities, as well as differences in task characteristics. Noise reduction did not have a significant effect on subsequent memory recall. This may be due to the background noise not having a detrimental effect on attentional processing at the favorable signal-to-noise ratio levels at which the test was conducted.

Low Latency Speech Enhancement for Hearing Aids Using Deep Filtering

Noise reduction is an important feature supporting hearing aid (HA) users in their daily routines and is thus included in most commercially available devices. Latency requirements of HAs require short processing windows resulting in a poor frequency resolution in the whole processing chain including noise reduction. Previous studies have shown that deep neural network (DNN) based algorithms outperform conventional noise reduction algorithms especially for non-stationary noises. This study explores a DNN based noise reduction method using deep filtering targeted for wideband spectrograms given the employed HA filter bank. That is, we predict complex filter coefficients that are linearly applied to the noisy spectrum. We assess different filter sizes over time and frequency axis, and provide evidence for a superior performance over a complex ratio mask. Furthermore, we introduce a frequency response loss that operates on a per-frequency-band basis to fully utilize the deep filtering concept. We objectively demonstrate on-par performance with related state-of-the-art deep learning methods and show in a subjective user study that our method is perceptually preferred to existing HA noise reduction algorithms.

Hearing Aid Noise Reduction Lowers the Sustained Listening Effort During Continuous Speech in Noise-A Combined Pupillometry and EEG Study.

The investigation of auditory cognitive processes recently moved from strictly controlled, trial-based paradigms toward the presentation of continuous speech. This also allows the investigation of listening effort on larger time scales (i.e., sustained listening effort). Here, we investigated the modulation of sustained listening effort by a noise reduction algorithm as applied in hearing aids in a listening scenario with noisy continuous speech. The investigated directional noise reduction algorithm mainly suppresses noise from the background. We recorded the pupil size and the EEG in 22 participants with hearing loss who listened to audio news clips in the presence of background multi-talker babble noise. We estimated how noise reduction (off, on) and signal-to-noise ratio (SNR; +3 dB, +8 dB) affect pupil size and the power in the parietal EEG alpha band (i.e., parietal alpha power) as well as the behavioral performance. Our results show that noise reduction reduces pupil size, while there was no significant effect of the SNR. It is important to note that we found interactions of SNR and noise reduction, which suggested that noise reduction reduces pupil size predominantly under the lower SNR. Parietal alpha power showed a similar yet nonsignificant pattern, with increased power under easier conditions. In line with the participants' reports that one of the two presented talkers was more intelligible, we found a reduced pupil size, increased parietal alpha power, and better performance when people listened to the more intelligible talker. We show that the modulation of sustained listening effort (e.g., by hearing aid noise reduction) as indicated by pupil size and parietal alpha power can be studied under more ecologically valid conditions. Mainly concluded from pupil size, we demonstrate that hearing aid noise reduction lowers sustained listening effort. Our study approximates to real-world listening scenarios and evaluates the benefit of the signal processing as can be found in a modern hearing aid.

Effects of Hearing Aid Noise Reduction on Early and Late Cortical Representations of Competing Talkers in Noise.

ObjectivesPrevious research using non-invasive (magnetoencephalography, MEG) and invasive (electrocorticography, ECoG) neural recordings has demonstrated the progressive and hierarchical representation and processing of complex multi-talker auditory scenes in the auditory cortex. Early responses (<85 ms) in primary-like areas appear to represent the individual talkers with almost equal fidelity and are independent of attention in normal-hearing (NH) listeners. However, late responses (>85 ms) in higher-order non-primary areas selectively represent the attended talker with significantly higher fidelity than unattended talkers in NH and hearing–impaired (HI) listeners. Motivated by these findings, the objective of this study was to investigate the effect of a noise reduction scheme (NR) in a commercial hearing aid (HA) on the representation of complex multi-talker auditory scenes in distinct hierarchical stages of the auditory cortex by using high-density electroencephalography (EEG).DesignWe addressed this issue by investigating early (<85 ms) and late (>85 ms) EEG responses recorded in 34 HI subjects fitted with HAs. The HA noise reduction (NR) was either on or off while the participants listened to a complex auditory scene. Participants were instructed to attend to one of two simultaneous talkers in the foreground while multi-talker babble noise played in the background (+3 dB SNR). After each trial, a two-choice question about the content of the attended speech was presented.ResultsUsing a stimulus reconstruction approach, our results suggest that the attention-related enhancement of neural representations of target and masker talkers located in the foreground, as well as suppression of the background noise in distinct hierarchical stages is significantly affected by the NR scheme. We found that the NR scheme contributed to the enhancement of the foreground and of the entire acoustic scene in the early responses, and that this enhancement was driven by better representation of the target speech. We found that the target talker in HI listeners was selectively represented in late responses. We found that use of the NR scheme resulted in enhanced representations of the target and masker speech in the foreground and a suppressed representation of the noise in the background in late responses. We found a significant effect of EEG time window on the strengths of the cortical representation of the target and masker.ConclusionTogether, our analyses of the early and late responses obtained from HI listeners support the existing view of hierarchical processing in the auditory cortex. Our findings demonstrate the benefits of a NR scheme on the representation of complex multi-talker auditory scenes in different areas of the auditory cortex in HI listeners.

Neural Representation Enhanced for Speech and Reduced for Background Noise With a Hearing Aid Noise Reduction Scheme During a Selective Attention Task.

ObjectivesSelectively attending to a target talker while ignoring multiple interferers (competing talkers and background noise) is more difficult for hearing-impaired (HI) individuals compared to normal-hearing (NH) listeners. Such tasks also become more difficult as background noise levels increase. To overcome these difficulties, hearing aids (HAs) offer noise reduction (NR) schemes. The objective of this study was to investigate the effect of NR processing (inactive, where the NR feature was switched off, vs. active, where the NR feature was switched on) on the neural representation of speech envelopes across two different background noise levels [+3 dB signal-to-noise ratio (SNR) and +8 dB SNR] by using a stimulus reconstruction (SR) method.DesignTo explore how NR processing supports the listeners’ selective auditory attention, we recruited 22 HI participants fitted with HAs. To investigate the interplay between NR schemes, background noise, and neural representation of the speech envelopes, we used electroencephalography (EEG). The participants were instructed to listen to a target talker in front while ignoring a competing talker in front in the presence of multi-talker background babble noise.ResultsThe results show that the neural representation of the attended speech envelope was enhanced by the active NR scheme for both background noise levels. The neural representation of the attended speech envelope at lower (+3 dB) SNR was shifted, approximately by 5 dB, toward the higher (+8 dB) SNR when the NR scheme was turned on. The neural representation of the ignored speech envelope was modulated by the NR scheme and was mostly enhanced in the conditions with more background noise. The neural representation of the background noise was modulated (i.e., reduced) by the NR scheme and was significantly reduced in the conditions with more background noise. The neural representation of the net sum of the ignored acoustic scene (ignored talker and background babble) was not modulated by the NR scheme but was significantly reduced in the conditions with a reduced level of background noise. Taken together, we showed that the active NR scheme enhanced the neural representation of both the attended and the ignored speakers and reduced the neural representation of background noise, while the net sum of the ignored acoustic scene was not enhanced.ConclusionAltogether our results support the hypothesis that the NR schemes in HAs serve to enhance the neural representation of speech and reduce the neural representation of background noise during a selective attention task. We contend that these results provide a neural index that could be useful for assessing the effects of HAs on auditory and cognitive processing in HI populations.

Impact of hearing aid noise reduction algorithms on the speech-evoked auditory brainstem response

The purposes of this study are to investigate the neural representation of a speech stimulus in the auditory system of individuals with normal hearing (NH) and those with hearing aids (HAs) and to explore the impact of noise reduction algorithms (NR) on auditory brainstem response to complex sounds (cABR). Twenty NH individuals and 28 HA users completed puretone audiometry, the Korean version of the Hearing in Noise Test (K-HINT), and cABR. In 0 and +5 dB signal-to-noise ratios (SNRs), the NH group was tested in /da/ only (quiet) and /da/ with white noise (WN) conditions while the HA group was tested in /da/ only, /da/ WN, /da/ WN NR ON, and /da/ WN NR OFF conditions. Significant differences were observed between /da/ only and /da/ WN conditions for F0 in both groups, but no SNR effect was observed for both groups. Findings of this study are consistent with previous literature that diminished cABR amplitudes indicate reduced representation of sounds in the auditory system. This is the first to examine the effect of a specific HA feature on cABR responses.

Pitch-Based Voice Activity Detection for Feedback Cancellation and Noise Reduction in Hearing Aids

In hearing aid (HA) systems, amplification of speech signals is done to compensate the hearing loss of patients. Background noise and feedback signals may also get amplified which degrade the intelligibility and quality of speech. To achieve high de-noise efficiency, signal processing unit in HA system has voice activity detector (VAD). The conventional VAD detects voice based on zero crossing rate or energy of input signals. However, these methods cannot perform well at low SNR or non-stationary noise environments. Since pitch is a special characteristic of speech and is basically independent of noise intensity, VAD based on pitch can have high accuracy even when the noise spectrum is changing drastically. In this paper, pitch-based VAD is presented and its accuracy is checked against zero crossing rate-based VAD (ZCR-VAD). For noise reduction, an improved multi-band spectral over-subtraction algorithm is employed along with the high accurate pitch-based VAD. For feedback cancellation, the performance of adaptive algorithms like NLMS, RLS and affine projection (AP) algorithms with pitch-based VAD is compared and it is observed that AP is suitable for feedback cancellation. The proposed noise reduction and feedback cancellation algorithm with pitch-based VAD method is tested with NOIZEUS speech database and with real-time noisy speech signals. The simulation results show that the accuracy of pitch-VAD is about 23% higher than that of ZCR-VAD. The SNR for the proposed noise reduction and feedback cancellation with pitch-VAD method is improved by 10 dB than conventional spectral subtraction and adaptive algorithms. Mean opinion score (MOS) obtained for the proposed method is 4.3 out of 5.

The interaction between reverberation and digital noise reduction in hearing aids: Acoustic and behavioral effects

Digital noise reduction (DNR) is widely implemented in hearing aids to improve the signal-to-noise ratio (SNR) of speech-in-noise. To accomplish this, the DNR processor must be able to accurately discriminate the speech versus noise signals. Acoustically, reverberation causes the blending of the speech and noise signals. The purpose of the present experiment was to examine whether reverberation impacts the benefits of DNR processing. Speech stimuli were combined with white noise at multiple SNRs. Speech-in-noise signals were processed using virtual auditory space techniques to simulate reverberation times and with a DNR simulation that mimicked hearing aid processing based on spectral subtraction. Signals were acoustically analyzed to quantify changes in SNR as a result of DNR processing. As reverberant degradation increased, the improvement in SNR decreased. Behaviorally, hearing-impaired individuals listened to low-context sentences in noise with varying reverberation either with or without DNR processi...

Directional Processing and Noise Reduction in Hearing Aids: Individual and Situational Influences on Preferred Setting.

A better understanding of individual differences in hearing aid (HA) outcome is a prerequisite for more personalized HA fittings. Currently, knowledge of how different user factors relate to response to directional processing (DIR) and noise reduction (NR) is sparse. To extend a recent study linking preference for DIR and NR to pure-tone average hearing thresholds (PTA) and cognitive factors by investigating if (1) equivalent links exist for different types of DIR and NR, (2) self-reported noise sensitivity and personality can account for additional variability in preferred DIR and NR settings, and (3) spatial target speech configuration interacts with individual DIR preference. Using a correlational study design, overall preference for different combinations of DIR and NR programmed into a commercial HA was assessed in a complex speech-in-noise situation and related to PTA, cognitive function, and different personality traits. Sixty experienced HA users aged 60-82 yr with controlled variation in PTA and working memory capacity took part in this study. All of them had participated in the earlier study, as part of which they were tested on a measure of "executive control" tapping into cognitive functions such as working memory, mental flexibility, and selective attention. Six HA settings based on unilateral (within-device) or bilateral (across-device) DIR combined with inactive, moderate, or strong single-microphone NR were programmed into a pair of behind-the-ear HAs together with individually prescribed amplification. Overall preference was assessed using a free-field simulation of a busy cafeteria situation with either a single frontal talker or two talkers at ±30° azimuth as the target speech. In addition, two questionnaires targeting noise sensitivity and the "Big Five" personality traits were administered. Data were analyzed using multiple regression analyses and repeated-measures analyses of variance with a focus on potential interactions between the HA settings and user factors. Consistent with the earlier study, preferred HA setting was related to PTA and executive control. However, effects were weaker this time. Noise sensitivity and personality did not interact with HA settings. As expected, spatial target speech configuration influenced preference, with bilateral and unilateral DIR "winning" in the single- and two-talker scenario, respectively. In general, participants with higher PTA tended to more strongly prefer bilateral DIR than participants with lower PTA. Although the current study lends some support to the view that PTA and cognitive factors affect preferred DIR and NR setting, it also indicates that these effects can vary across noise management technologies. To facilitate more personalized HA fittings, future research should investigate the source of this variability.

Fitting Noise Management Signal Processing Applying the American Academy of Audiology Pediatric Amplification Guideline: Verification Protocols.

Although guidelines for fitting hearing aids for children are well developed and have strong basis in evidence, specific protocols for fitting and verifying some technologies are not always available. One such technology is noise management in children's hearing aids. Children are frequently in high-level and/or noisy environments, and many options for noise management exist in modern hearing aids. Verification protocols are needed to define specific test signals and levels for use in clinical practice. This work aims to (1) describe the variation in different brands of noise reduction processors in hearing aids and the verification of these processors and (2) determine whether these differences are perceived by 13 children who have hearing loss. Finally, we aimed to develop a verification protocol for use in pediatric clinical practice. A set of hearing aids was tested using both clinically available test systems and a reference system, so that the impacts of noise reduction signal processing in hearing aids could be characterized for speech in a variety of background noises. A second set of hearing aids was tested across a range of audiograms and across two clinical verification systems to characterize the variance in clinical verification measurements. Finally, a set of hearing aid recordings that varied by type of noise reduction was rated for sound quality by children with hearing loss. Significant variation across makes and models of hearing aids was observed in both the speed of noise reduction activation and the magnitude of noise reduction. Reference measures indicate that noise-only testing may overestimate noise reduction magnitude compared to speech-in-noise testing. Variation across clinical test signals was also observed, indicating that some test signals may be more successful than others for characterization of hearing aid noise reduction. Children provided different sound quality ratings across hearing aids, and for one hearing aid rated the sound quality as higher with the noise reduction system activated. Implications for clinical verification systems may be that greater standardization and the use of speech-in-noise test signals may improve the quality and consistency of noise reduction verification cross clinics. A suggested clinical protocol for verification of noise management in children's hearing aids is suggested.

Using Speech Recall in Hearing Aid Fitting and Outcome Evaluation Under Ecological Test Conditions

In adaptive Speech Reception Threshold (SRT) tests used in the audiological clinic, speech is presented at signal to noise ratios (SNRs) that are lower than those generally encountered in real-life communication situations. At higher, ecologically valid SNRs, however, SRTs are insensitive to changes in hearing aid signal processing that may be of benefit to listeners who are hard of hearing. Previous studies conducted in Swedish using the Sentence-final Word Identification and Recall test (SWIR) have indicated that at such SNRs, the ability to recall spoken words may be a more informative measure. In the present study, a Danish version of SWIR, known as the Sentence-final Word Identification and Recall Test in a New Language (SWIRL) was introduced and evaluated in two experiments. The objective of experiment 1 was to determine if the Swedish results demonstrating benefit from noise reduction signal processing for hearing aid wearers could be replicated in 25 Danish participants with mild to moderate symmetrical sensorineural hearing loss. The objective of experiment 2 was to compare direct-drive and skin-drive transmission in 16 Danish users of bone-anchored hearing aids with conductive hearing loss or mixed sensorineural and conductive hearing loss. In experiment 1, performance on SWIRL improved when hearing aid noise reduction was used, replicating the Swedish results and generalizing them across languages. In experiment 2, performance on SWIRL was better for direct-drive compared with skin-drive transmission conditions. These findings indicate that spoken word recall can be used to identify benefits from hearing aid signal processing at ecologically valid, positive SNRs where SRTs are insensitive.

An Improved Algorithm for Noise Reduction in Hearing Aids

An Improved Algorithm for Noise Reduction in Hearing Aids

Directivity and noise reduction in hearing aids: speech perception and benefit

AbstractHearing aid. AimTo compare the performance, benefit and satisfaction of users of ITE, CIC and BTE digital hearing aid with noise reduction and omnidirectional and directional microphones. Method34 users of hearing aid were evaluated by means of speech perception in noise tests and APHAB and IOI self assessment questionnaires. Prospective study. ResultsBetter results were obtained by users of ITE, CIC and directional hearing aids, however, no statistical significance was found between the groups. ConclusionDirectivity improved speech perception in noise and benefit in daily life situations.

Two-Stage Filter-Bank System for Improved Single-Channel Noise Reduction in Hearing Aids

The filter-bank system implemented in hearing aids has to fulfill various constraints such as low latency and high stop-band attenuation, usually at the cost of low frequency resolution. In the context of frequency-domain noise-reduction algorithms, insufficient frequency resolution may lead to annoying residual noise artifacts since the spectral harmonics of the speech cannot properly be resolved. Especially in case of female speech signals, the noise between the spectral harmonics causes a distinct roughness of the processed signals. Therefore, this work proposes a two-stage filter-bank system, such that the frequency resolution can be improved for the purpose of noise reduction, while the original first-stage hearing-aid filter-bank system can still be used for compression and amplification. We also propose methods to implement the second filter-bank stage with little additional algorithmic delay. Furthermore, the computational complexity is an important design criterion. This finally leads to an application of the second filter-bank stage to lower frequency bands only, resulting in the ability to resolve the harmonics of speech. The paper presents a systematic description of the second filter-bank stage, discusses its influence on the processed signals in detail and further presents the results of a listening test which indicates the improved performance compared to the original single-stage filter-bank system.

The Effect of Hearing Aid Noise Reduction on Listening Effort in Hearing-Impaired Adults

The purpose of the present study was to evaluate the effect of a noise-reduction (NR) algorithm on the listening effort hearing-impaired participants expend on a speech in noise task. Twelve hearing-impaired listeners fitted with behind-the-ear hearing aids with a fast-acting modulation-based NR algorithm participated in this study. A dual-task paradigm was used to measure listening effort with and without the NR enabled in the hearing aid. The primary task was a sentence-in-noise task presented at fixed overall speech performance levels of 76% (moderate listening condition) and 50% (difficult listening condition) correct performance, and the secondary task was a visual-tracking test. Participants also completed measures of working memory (Reading Span test), and processing speed (Digit Symbol Substitution Test) ability. Participants' speech recognition in noise scores did not significantly change with the NR algorithm activated in the hearing aid in either listening condition. The NR algorithm significantly decreased listening effort, but only in the more difficult listening condition. Last, there was a tendency for participants with faster processing speeds to expend less listening effort with the NR algorithm when listening to speech in background noise in the difficult listening condition. The NR algorithm reduced the listening effort adults with hearing loss must expend to understand speech in noise.

Detection threshold for sound distortion resulting from noise reduction in normal-hearing and hearing-impaired listeners.

Hearing-aid noise reduction should reduce background noise, but not disturb the target speech. This objective is difficult because noise reduction suffers from a trade-off between the amount of noise removed and signal distortion. It is unknown if this important trade-off differs between normal-hearing (NH) and hearing-impaired (HI) listeners. This study separated the negative effect of noise reduction (distortion) from the positive effect (reduction of noise) to allow the measurement of the detection threshold for noise-reduction (NR) distortion. Twelve NH subjects and 12 subjects with mild to moderate sensorineural hearing loss participated in this study. The detection thresholds for distortion were determined using an adaptive procedure with a three-interval, two-alternative forced-choice paradigm. Different levels of distortion were obtained by changing the maximum amount of noise reduction. Participants were also asked to indicate their preferred NR strength. The detection threshold for overall distortion was higher for HI subjects than for NH subjects, suggesting that stronger noise reduction can be applied for HI listeners without affecting the perceived sound quality. However, the preferred NR strength of HI listeners was closer to their individual detection threshold for distortion than in NH listeners. This implies that HI listeners tolerate fewer audible distortions than NH listeners.