scholarly journals Smartphone-Based System for Learning and Inferring Hearing Aid Settings

2016 ◽  
Vol 27 (09) ◽  
pp. 732-749 ◽  
Author(s):  
Gabriel Aldaz ◽  
Sunil Puria ◽  
Larry J. Leifer
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
User Study ◽  
Hearing Aid ◽  
Sensor Data ◽  
Significant Preference ◽  
Environmental Sound ◽  
Sound Environment ◽  
Study Participants ◽  
Hearing System

Background: Previous research has shown that hearing aid wearers can successfully self-train their instruments’ gain-frequency response and compression parameters in everyday situations. Combining hearing aids with a smartphone introduces additional computing power, memory, and a graphical user interface that may enable greater setting personalization. To explore the benefits of self-training with a smartphone-based hearing system, a parameter space was chosen with four possible combinations of microphone mode (omnidirectional and directional) and noise reduction state (active and off). The baseline for comparison was the “untrained system,” that is, the manufacturer’s algorithm for automatically selecting microphone mode and noise reduction state based on acoustic environment. The “trained system” first learned each individual’s preferences, self-entered via a smartphone in real-world situations, to build a trained model. The system then predicted the optimal setting (among available choices) using an inference engine, which considered the trained model and current context (e.g., sound environment, location, and time). Purpose: To develop a smartphone-based prototype hearing system that can be trained to learn preferred user settings. Determine whether user study participants showed a preference for trained over untrained system settings. Research Design: An experimental within-participants study. Participants used a prototype hearing system—comprising two hearing aids, Android smartphone, and body-worn gateway device—for ˜6 weeks. Study Sample: Sixteen adults with mild-to-moderate sensorineural hearing loss (HL) (ten males, six females; mean age = 55.5 yr). Fifteen had ≥6 mo of experience wearing hearing aids, and 14 had previous experience using smartphones. Intervention: Participants were fitted and instructed to perform daily comparisons of settings (“listening evaluations”) through a smartphone-based software application called Hearing Aid Learning and Inference Controller (HALIC). In the four-week-long training phase, HALIC recorded individual listening preferences along with sensor data from the smartphone—including environmental sound classification, sound level, and location—to build trained models. In the subsequent two-week-long validation phase, participants performed blinded listening evaluations comparing settings predicted by the trained system (“trained settings”) to those suggested by the hearing aids’ untrained system (“untrained settings”). Data Collection and Analysis: We analyzed data collected on the smartphone and hearing aids during the study. We also obtained audiometric and demographic information. Results: Overall, the 15 participants with valid data significantly preferred trained settings to untrained settings (paired-samples t test). Seven participants had a significant preference for trained settings, while one had a significant preference for untrained settings (binomial test). The remaining seven participants had nonsignificant preferences. Pooling data across participants, the proportion of times that each setting was chosen in a given environmental sound class was on average very similar. However, breaking down the data by participant revealed strong and idiosyncratic individual preferences. Fourteen participants reported positive feelings of clarity, competence, and mastery when training via HALIC. Conclusions: The obtained data, as well as subjective participant feedback, indicate that smartphones could become viable tools to train hearing aids. Individuals who are tech savvy and have milder HL seem well suited to take advantages of the benefits offered by training with a smartphone.

Performance Improvement of Digital Hearing Aid Systems

2015 ◽  
Vol 1 ◽  
pp. 27
Author(s):  
Isiaka Ajewale Alimi
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Digital Signal Processor ◽  
Speech Intelligibility ◽  
Communication Systems ◽  
Hearing Aid ◽  
Spectral Subtraction ◽  
Residual Spectrum ◽  
Digital Hearing Aids ◽  
Digital Hearing Aid

Digital hearing aids addresses the issues of noise and speech intelligibility that is associated with the analogue types. One of the main functions of the digital signal processor (DSP) of digital hearing aid systems is noise reduction which can be achieved by speech enhancement algorithms which in turn improve system performance and flexibility. However, studies have shown that the quality of experience (QoE) with some of the current hearing aids is not up to expectation in a noisy environment due to interfering sound, background noise and reverberation. It is also suggested that noise reduction features of the DSP can be further improved accordingly. Recently, we proposed an adaptive spectral subtraction algorithm to enhance the performance of communication systems and address the issue of associated musical noise generated by the conventional spectral subtraction algorithm. The effectiveness of the algorithm has been confirmed by different objective and subjective evaluations. In this study, an adaptive spectral subtraction algorithm is implemented using the noise-estimation algorithm for highly non-stationary noisy environments instead of the voice activity detection (VAD) employed in our previous work due to its effectiveness. Also, signal to residual spectrum ratio (SR) is implemented in order to control the amplification distortion for speech intelligibility improvement. The results show that the proposed scheme gives comparatively better performance and can be easily employed in digital hearing aid system for improving speech quality and intelligibility.

Evaluation of a Transient Noise Reduction Strategy for Hearing Aids

2012 ◽  
Vol 23 (08) ◽  
pp. 606-615 ◽  
Author(s):  
HaiHong Liu ◽  
Hua Zhang ◽  
Ruth A. Bentler ◽  
Demin Han ◽  
Luo Zhang
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Background Noise ◽  
Hearing Aid ◽  
Sound Quality ◽  
T Test ◽  
Reduction Strategy ◽  
Speech Stimuli ◽  
Significant Difference ◽  
Speech Clarity

Background: Transient noise can be disruptive for people wearing hearing aids. Ideally, the transient noise should be detected and controlled by the signal processor without disrupting speech and other intended input signals. A technology for detecting and controlling transient noises in hearing aids was evaluated in this study. Purpose: The purpose of this study was to evaluate the effectiveness of a transient noise reduction strategy on various transient noises and to determine whether the strategy has a negative impact on sound quality of intended speech inputs. Research Design: This was a quasi-experimental study. The study involved 24 hearing aid users. Each participant was asked to rate the parameters of speech clarity, transient noise loudness, and overall impression for speech stimuli under the algorithm-on and algorithm-off conditions. During the evaluation, three types of stimuli were used: transient noises, speech, and background noises. The transient noises included “knife on a ceramic board,” “mug on a tabletop,” “office door slamming,” “car door slamming,” and “pen tapping on countertop.” The speech sentences used for the test were presented by a male speaker in Mandarin. The background noises included “party noise” and “traffic noise.” All of these sounds were combined into five listening situations: (1) speech only, (2) transient noise only, (3) speech and transient noise, (4) background noise and transient noise, and (5) speech and background noise and transient noise. Results: There was no significant difference on the ratings of speech clarity between the algorithm-on and algorithm-off (t-test, p = 0.103). Further analysis revealed that speech clarity was significant better at 70 dB SLP than 55 dB SPL (p < 0.001). For transient noise loudness: under the algorithm-off condition, the percentages of subjects rating the transient noise to be somewhat soft, appropriate, somewhat loud, and too loud were 0.2, 47.1, 29.6, and 23.1%, respectively. The corresponding percentages under the algorithm-on were 3.0, 72.6, 22.9, and 1.4%, respectively. A significant difference on the ratings of the transient noise loudness was found between the algorithm-on and algorithm-off (t-test, p < 0.001). For overall impression for speech stimuli: under the algorithm-off condition, the percentage of subjects rating the algorithm to be not helpful at all, somewhat helpful, helpful, and very helpful for speech stimuli were 36.5, 20.8, 33.9, and 8.9%, respectively. Under the algorithm-on condition, the corresponding percentages were 35.0, 19.3, 30.7, and 15.0%, respectively. Statistical analysis revealed there was a significant difference on the ratings of overall impression on speech stimuli. The ratings under the algorithm-on condition were significantly more helpful for speech understanding than the ratings under algorithm-off (t-test, p < 0.001). Conclusions: The transient noise reduction strategy appropriately controlled the loudness for most of the transient noises and did not affect the sound quality, which could be beneficial to hearing aid wearers.

scholarly journals Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises

2020 ◽  
Vol 10 (17) ◽  
pp. 6077
Author(s):  
Gyuseok Park ◽  
Woohyeong Cho ◽  
Kyu-Sung Kim ◽  
Sangmin Lee
Keyword(s):  
Neural Networks ◽  
Noise Reduction ◽  
Speech Enhancement ◽  
Hearing Aids ◽  
Deep Neural Networks ◽  
Hearing Aid ◽  
Environmental Noise ◽  
Speech Quality ◽  
Impaired Hearing ◽  
Noise Classification

Hearing aids are small electronic devices designed to improve hearing for persons with impaired hearing, using sophisticated audio signal processing algorithms and technologies. In general, the speech enhancement algorithms in hearing aids remove the environmental noise and enhance speech while still giving consideration to hearing characteristics and the environmental surroundings. In this study, a speech enhancement algorithm was proposed to improve speech quality in a hearing aid environment by applying noise reduction algorithms with deep neural network learning based on noise classification. In order to evaluate the speech enhancement in an actual hearing aid environment, ten types of noise were self-recorded and classified using convolutional neural networks. In addition, noise reduction for speech enhancement in the hearing aid were applied by deep neural networks based on the noise classification. As a result, the speech quality based on the speech enhancements removed using the deep neural networks—and associated environmental noise classification—exhibited a significant improvement over that of the conventional hearing aid algorithm. The improved speech quality was also evaluated by objective measure through the perceptual evaluation of speech quality score, the short-time objective intelligibility score, the overall quality composite measure, and the log likelihood ratio score.

scholarly journals Sentence Recognition in Noise and Perceived Benefit of Noise Reduction on the Receiver and Transmitter Sides of a BICROS Hearing Aid

2013 ◽  
Vol 24 (10) ◽  
pp. 980-991 ◽  
Author(s):  
Kristi Oeding ◽  
Michael Valente
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Repeated Measures ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Hearing Aid ◽  
Directional Microphones ◽  
Sentence Recognition ◽  
Clinically Significant ◽  
The Impact

Background: In the past, bilateral contralateral routing of signals (BICROS) amplification incorporated omnidirectional microphones on the transmitter and receiver sides and some models utilized noise reduction (NR) on the receiver side. Little research has examined the performance of BICROS amplification in background noise. However, previous studies examining contralateral routing of signals (CROS) amplification have reported that the presence of background noise on the transmitter side negatively affected speech recognition. Recently, NR was introduced as a feature on the receiver and transmitter sides of BICROS amplification, which has the potential to decrease the impact of noise on the wanted speech signal by decreasing unwanted noise directed to the transmitter side. Purpose: The primary goal of this study was to examine differences in the reception threshold for sentences (RTS in dB) using the Hearing in Noise Test (HINT) in a diffuse listening environment between unaided and three aided BICROS conditions (no NR, mild NR, and maximum NR) in the Tandem 16 BICROS. A secondary goal was to examine real-world subjective impressions of the Tandem 16 BICROS compared to unaided. Research Design: A randomized block repeated measures single blind design was used to assess differences between no NR, mild NR, and maximum NR listening conditions. Study Sample: Twenty-one adult participants with asymmetric sensorineural hearing loss (ASNHL) and experience with BICROS amplification were recruited from Washington University in St. Louis School of Medicine. Data Collection and Analysis: Participants were fit with the National Acoustic Laboratories’ Nonlinear version 1 prescriptive target (NAL-NL1) with the Tandem 16 BICROS at the initial visit and then verified using real-ear insertion gain (REIG) measures. Participants acclimatized to the Tandem 16 BICROS for 4 wk before returning for final testing. Participants were tested utilizing HINT sentences examining differences in RTS between unaided and three aided listening conditions. Subjective benefit was determined via the Abbreviated Profile of Hearing Aid Benefit (APHAB) questionnaire between the Tandem 16 BICROS and unaided. A repeated measures analysis of variance (ANOVA) was utilized to analyze the results of the HINT and APHAB. Results: Results revealed no significant differences in the RTS between unaided, no NR, mild NR, and maximum NR. Subjective impressions using the APHAB revealed statistically and clinically significant benefit with the Tandem 16 BICROS compared to unaided for the Ease of Communication (EC), Background Noise (BN), and Reverberation (RV) subscales. Conclusions: The RTS was not significantly different between unaided, no NR, mild NR, and maximum NR. None of the three aided listening conditions were significantly different from unaided performance as has been reported for previous studies examining CROS hearing aids. Further, based on comments from participants and previous research studies with conventional hearing aids, manufacturers of BICROS amplification should consider incorporating directional microphones and independent volume controls on the receiver and transmitter sides to potentially provide further improvement in signal-to-noise ratio (SNR) for patients with ASNHL.

Hearing Impaired Children's Preference for, and Performance with, Four Combinations of Directional Microphone and Digital Noise Reduction Technology

2013 ◽  
Vol 24 (09) ◽  
pp. 832-844 ◽  
Author(s):  
Andrea L. Pittman ◽  
Mollie M. Hiipakka
Keyword(s):  
Hearing Loss ◽  
Speech Perception ◽  
Noise Reduction ◽  
Hearing Aids ◽  
Hearing Aid ◽  
Directional Hearing ◽  
Short Period ◽  
Children With Hearing Loss ◽  
And Performance ◽  
Noise Management

Background: Before advanced noise-management features can be recommended for use in children with hearing loss, evidence regarding their ability to use these features to optimize speech perception is necessary. Purpose: The purpose of this study was to examine the relation between children's preference for, and performance with, four combinations of noise-management features in noisy listening environments. Research Design: Children with hearing loss were asked to repeat short sentences presented in steady-state noise or in multitalker babble while wearing ear-level hearing aids. The aids were programmed with four memories having an orthogonal arrangement of two noise-management features. The children were also asked to indicate the hearing aid memory that they preferred in each of the listening conditions both initially and after a short period of use. Study Sample: Fifteen children between the ages of 8 and 12 yr with moderate hearing losses, bilaterally. Results: The children's preference for noise management aligned well with their performance for at least three of the four listening conditions. The configuration of noise-management features had little effect on speech perception with the exception of reduced performance for speech originating from behind the child while in a directional hearing aid setting. Additionally, the children's preference appeared to be governed by listening comfort, even under conditions for which a benefit was not expected such as the use of digital noise reduction in the multitalker babble conditions. Conclusions: The results serve as evidence in support of the use of noise-management features in grade-school children as young as 8 yr of age.

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

2016 ◽  
Vol 27 (03) ◽  
pp. 237-251 ◽  
Author(s):  
Susan Scollie ◽  
Charla Levy ◽  
Nazanin Pourmand ◽  
Parvaneh Abbasalipour ◽  
Marlene Bagatto ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Clinical Practice ◽  
Noise Reduction ◽  
Hearing Aids ◽  
Hearing Aid ◽  
Sound Quality ◽  
Speech In Noise ◽  
Verification Systems ◽  
Clinical Verification ◽  
Noise Management

Background: 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. Purpose: 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. Study Sample: 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. Results: 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. Conclusions: 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.

scholarly journals Changes in Psychosocial Measures After a 6-Week Field Trial

2017 ◽  
Vol 26 (2) ◽  
pp. 119-128 ◽  
Author(s):  
Jamie L. Desjardins ◽  
Karen A. Doherty
Keyword(s):  
Hearing Loss ◽  
Hearing Aids ◽  
Field Trial ◽  
Hearing Aid ◽  
Control Group ◽  
Psychosocial Consequences ◽  
Hearing Handicap ◽  
Bilateral Sensorineural Hearing Loss ◽  
Study Participants ◽  
Positive Effect

PurposeThe purpose of this study was to assess the extent to which intervention with hearing aids, namely, a 6-week hearing aid field trial, can minimize the psychosocial consequences of hearing loss in adults who have previously not sought treatment for their hearing loss.MethodTwenty-four adults with mild to moderate bilateral sensorineural hearing loss, who had never worn hearing aids or sought help for their hearing loss, participated in this study. Participants were fitted with receiver-in-canal hearing aids, bilaterally, and wore them for 6 weeks. Participants completed subjective measures of hearing handicap and attitudes about hearing loss and hearing aids before, during, and after the hearing aid trial. A control group of age-matched participants followed the same experimental protocol, except they were not fitted with hearing aids.ResultsUsing hearing aids for 6 weeks significantly reduced participants' perceived stigma of hearing aids, personal distress and inadequacy due to hearing difficulties, and hearing handicap.ConclusionsA hearing aid trial can have a positive effect on a person's attitudes toward wearing hearing aids and decrease hearing handicap.

scholarly journals Self-Assessment of Hearing and Purchase of Hearing Aids by Middle-Aged and Elderly Adults

2015 ◽  
Vol 20 (01) ◽  
pp. 048-053 ◽  
Author(s):  
Andressa Otavio ◽  
Patricia Coradini ◽  
Adriane Teixeira
Keyword(s):  
Hearing Aids ◽  
Hearing Aid ◽  
Diagnostic Interview ◽  
Elderly Adults ◽  
Self Assessment ◽  
Cross Sectional ◽  
Assessment Scores ◽  
Hearing Ability ◽  
Study Participants ◽  
Selection Of

Introduction Presbycusis is a consequence of aging. Prescription of hearing aids is part of the treatment, although the prevalence of use by elderly people is still small. Objective To verify whether or not self-assessment of hearing is a predictor for purchase of hearing aids. Methods Quantitative, cross-sectional, descriptive, and observational study. Participants were subjects who sought a private hearing center for selection of hearing aids. During the diagnostic interview, subjects answered the following question: “On a scale of 1 to 10, with 1 being the worst and 10 the best, how would you rate your overall hearing ability?” After that, subjects underwent audiometry, selected a hearing aid, performed a home trial, and decided whether or not to purchase the hearing aid. The variables were associated and analyzed statistically. Results The sample was comprised of 32 subjects, both men and women, with a higher number of women. Mean age was 71.41 ± 12.14 years. Self-assessment of hearing ranged from 2 to 9 points. Overall, 71.9% of the subjects purchased hearing aids. There was no association between scores in the self-assessment and the purchase of hearing aids (p = 0.263). Among those who scored between 2 and 5 points, 64.7% purchased the device; between 6 and 7 points, 76.09% purchased the device; and between 8 and 9 points, 50% purchased the device, respectively. Conclusion There is evidence that low self-assessment scores lead to the purchase of hearing aids, although no significant association was observed in the sample.

scholarly journals The Acoustic Environments in Which Older Adults Wear Their Hearing Aids: Insights From Datalogging Sound Environment Classification

2018 ◽  
Vol 27 (4) ◽  
pp. 594-603 ◽  
Author(s):  
Larry E. Humes ◽  
Sara E. Rogers ◽  
Anna K. Main ◽  
Dana L. Kinney
Keyword(s):  
Older Adults ◽  
Hearing Aids ◽  
Hearing Aid ◽  
Social Activity ◽  
Automatic Classification ◽  
Acoustic Environment ◽  
Age Related ◽  
Sound Environment ◽  
Acoustic Environments

Purpose This report presents data on the acoustic environments in which older adults with age-related hearing loss wear their hearing aids. Method This is an observational study providing descriptive data from 2 primary datasets: (a) 128 older adults wearing hearing aids for an average of 6 weeks and (b) 65 older adults wearing hearing aids for an average of 13 months. Acoustic environments were automatically and continuously classified about every 4 s, using the hearing aids' signal processing, into 1 of 7 acoustic environment categories. Results For both groups, older adults wore their hearing aids about 60% of the time in quiet or speech-only conditions. The automatic classification of sound environments was shown to be reliable over relatively short (6-week) and long (13-month) durations. Moreover, the results were shown to have some validity in that the obtained acoustic environment profiles matched a self-reported measure of social activity administered prior to hearing aid usage. For a subset of 56 older adults with data from both the 6-week and 13-month wear times, the daily amount of hearing aid usage diminished but the profile of sound environments frequented by the wearers remained stable. Conclusions Examination of the results from the automatic classification of sound environments by the hearing aids of older adults provides reliable and valid environment classifications. The present data indicate that most such wearers choose generally favorable acoustic environments for hearing aid use.

Effect of Maximum Power Output and Noise Reduction on Speech Recognition in Noise

2011 ◽  
Vol 22 (05) ◽  
pp. 265-273 ◽  
Author(s):  
Francis Kuk ◽  
Heidi Peeters ◽  
Chi Lau ◽  
Petri Korhonen
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Power Output ◽  
Noise Level ◽  
Repeated Measures ◽  
Speech Intelligibility ◽  
Hearing Aid ◽  
Maximum Power ◽  
Maximum Power Output ◽  
Bonferroni Adjustment

Background: The maximum power output (MPO) of a hearing aid was typically discussed in the context of avoiding loudness discomfort. However, an MPO that is too low, as in the cases to avoid discomfort for people with a severe loudness tolerance problem and hearing losses that exceed the fitting range of the hearing aids, could negatively affect sound quality and speech intelligibility in noise. Purpose: The current study was designed to demonstrate the degradation in speech intelligibility in noise on the HINT (Hearing in Noise Test) when the MPO of the wearers' hearing aids was lowered by 10 dB from the default. The interactions with noise reduction (NR) algorithms (classic [NR-classic] and Speech Enhancer [NR-SE]) were also examined. Research Design: A single-blinded, factorial repeated-measures design was used to study the effect of noise input level (68 dBC, 75 dBC), MPO setting (default and default-10), and NR algorithm (off, classic, SE) on HINT performance. Study Sample: Eleven adults with a severe sensorineural hearing loss participated. Intervention: Participants were fit with the Widex m4-19 behind-the-ear hearing aids binaurally in the default frequency response and MPO settings. The hearing aids were adjusted to six MPO (default, default-10) by NR (off, classic, SE conditions). Testing was completed within one 2 hr session. Data Collection and Analysis: The RTS (reception threshold for speech) for 50% correct on the HINT was measured in each of the six hearing aid conditions at two input levels (68 and 75 dBC) with speech and noise stimuli presented from the front. Repeated-measures ANOVAs were conducted using SPSS software to examine significant differences. Results: A repeated-measures ANOVA showed that noise level was not significant while NR algorithm and MPO were significant. The interaction between noise level and NR algorithm was also significant. Post hoc analysis with Bonferroni adjustment for the effect of NR algorithm showed that performance with NR-off was significantly poorer than performance with NR-classic and NR-SE (p < 0.05). However, NR-classic and NR-SE were not significantly different from each other (p > 0.05). Conclusions: An MPO that was 10 dB lower than the default could negatively affect the signal-to-noise ratio (SNR) of the listening environment. However, NR could compensate for the degradation in SNR.

Sign in / Sign up

Export Citation Format

Share Document