scholarly journals Improving Speech Understanding and Monitoring Health with Hearing Aids Using Artificial Intelligence and Embedded Sensors

2021 ◽  
Vol 42 (03) ◽  
pp. 295-308
Author(s):  
David A. Fabry ◽  
Achintya K. Bhowmik
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Hearing Aids ◽  
Background Noise ◽  
Speech Intelligibility ◽  
Hearing Aid ◽  
Machine Learning Algorithms ◽  
Embedded Sensors ◽  
Health And Wellness ◽  
Speech Understanding

AbstractThis article details ways that machine learning and artificial intelligence technologies are being integrated in modern hearing aids to improve speech understanding in background noise and provide a gateway to overall health and wellness. Discussion focuses on how Starkey incorporates automatic and user-driven optimization of speech intelligibility with onboard hearing aid signal processing and machine learning algorithms, smartphone-based deep neural network processing, and wireless hearing aid accessories. The article will conclude with a review of health and wellness tracking capabilities that are enabled by embedded sensors and artificial intelligence.

Evaluation of an Articulation-Index Based Model for Predicting the Effects of Adaptive Frequency Response Hearing Aids

1990 ◽  
Vol 33 (4) ◽  
pp. 676-689 ◽  
Author(s):  
David A. Fabry ◽  
Dianne J. Van Tasell
Keyword(s):  
Transfer Function ◽  
Frequency Response ◽  
Hearing Aids ◽  
Background Noise ◽  
Speech Intelligibility ◽  
Hearing Aid ◽  
Hearing Impaired ◽  
Articulation Index ◽  
Intelligibility Ratings ◽  
High Pass

The Articulation Index (AI) was used to evaluate an “adaptive frequency response” (AFR) hearing aid with amplification characteristics that automatically change to become more high-pass with increasing levels of background noise. Speech intelligibility ratings of connected discourse by normal-hearing subjects were predicted well by an empirically derived AI transfer function. That transfer function was used to predict aided speech intelligibility ratings by 12 hearing-impaired subjects wearing a master hearing aid with the Argosy Manhattan Circuit enabled (AFR-on) or disabled (AFR-off). For all subjects, the AI predicted no improvements in speech intelligibility for the AFR-on versus AFR-off condition, and no significant improvements in rated intelligibility were observed. The ability of the AI to predict aided speech intelligibility varied across subjects. However, ratings from every hearing-impaired subject were related monotonically to AI. Therefore, AI calculations may be used to predict relative—but not absolute—levels of speech intelligibility produced under different amplification conditions.

scholarly journals The Collaboration between Hearing Aid Users and Artificial Intelligence to Optimize Sound

2021 ◽  
Vol 42 (03) ◽  
pp. 282-294
Author(s):  
Laura Winther Balling ◽  
Lasse Lohilahti Mølgaard ◽  
Oliver Townend ◽  
Jens Brehm Bagger Nielsen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Signal Processing ◽  
Hearing Aids ◽  
Hearing Aid ◽  
Bayesian Optimization ◽  
Listening Environments ◽  
User Data ◽  
The Individual ◽  
Average User

AbstractHearing aid gain and signal processing are based on assumptions about the average user in the average listening environment, but problems may arise when the individual hearing aid user differs from these assumptions in general or specific ways. This article describes how an artificial intelligence (AI) mechanism that operates continuously on input from the user may alleviate such problems by using a type of machine learning known as Bayesian optimization. The basic AI mechanism is described, and studies showing its effects both in the laboratory and in the field are summarized. A crucial fact about the use of this AI is that it generates large amounts of user data that serve as input for scientific understanding as well as for the development of hearing aids and hearing care. Analyses of users' listening environments based on these data show the distribution of activities and intentions in situations where hearing is challenging. Finally, this article demonstrates how further AI-based analyses of the data can drive development.

Use of a Mild-Gain Hearing Aid by Middle-Age Normal-Hearing Adults Who Do and Do Not Self-Report Trouble Hearing in Background Noise

2020 ◽  
Vol 29 (3) ◽  
pp. 419-428
Author(s):  
Jasleen Singh ◽  
Karen A. Doherty
Keyword(s):  
Hearing Aids ◽  
Background Noise ◽  
Middle Age ◽  
Hearing Aid ◽  
Normal Hearing ◽  
Self Report ◽  
Hearing Aid Use ◽  
Hearing Handicap ◽  
Before And After ◽  
Hearing Problems

Purpose The aim of the study was to assess how the use of a mild-gain hearing aid can affect hearing handicap, motivation, and attitudes toward hearing aids for middle-age, normal-hearing adults who do and do not self-report trouble hearing in background noise. Method A total of 20 participants (45–60 years of age) with clinically normal-hearing thresholds (< 25 dB HL) were enrolled in this study. Ten self-reported difficulty hearing in background noise, and 10 did not self-report difficulty hearing in background noise. All participants were fit with mild-gain hearing aids, bilaterally, and were asked to wear them for 2 weeks. Hearing handicap, attitudes toward hearing aids and hearing loss, and motivation to address hearing problems were evaluated before and after participants wore the hearing aids. Participants were also asked if they would consider purchasing a hearing aid before and after 2 weeks of hearing aid use. Results After wearing the hearing aids for 2 weeks, hearing handicap scores decreased for the participants who self-reported difficulty hearing in background noise. No changes in hearing handicap scores were observed for the participants who did not self-report trouble hearing in background noise. The participants who self-reported difficulty hearing in background noise also reported greater personal distress from their hearing problems, were more motivated to address their hearing problems, and had higher levels of hearing handicap compared to the participants who did not self-report trouble hearing in background noise. Only 20% (2/10) of the participants who self-reported trouble hearing in background noise reported that they would consider purchasing a hearing aid after 2 weeks of hearing aid use. Conclusions The use of mild-gain hearing aids has the potential to reduce hearing handicap for normal-hearing, middle-age adults who self-report difficulty hearing in background noise. However, this may not be the most appropriate treatment option for their current hearing problems given that only 20% of these participants would consider purchasing a hearing aid after wearing hearing aids for 2 weeks.

Comparison of In-the-Ear and Over-the-Ear Hearing Aid Fittings

1986 ◽  
Vol 51 (4) ◽  
pp. 362-369 ◽  
Author(s):  
Donna M. Risberg ◽  
Robyn M. Cox
Keyword(s):  
Hearing Aids ◽  
High Frequency ◽  
Comparative Evaluation ◽  
Speech Intelligibility ◽  
Hearing Aid ◽  
Hearing Aid Fitting ◽  
The Difference ◽  
Functional Gain ◽  
The Relationship

A custom in-the-ear (ITE) hearing aid fitting was compared to two over-the-ear (OTE) hearing aid fittings for each of 9 subjects with mild to moderately severe hearing losses. Speech intelligibility via the three instruments was compared using the Speech Intelligibility Rating (SIR) test. The relationship between functional gain and coupler gain was compared for the ITE and the higher rated OTE instruments. The difference in input received at the microphone locations of the two types of hearing aids was measured for 10 different subjects and compared to the functional gain data. It was concluded that (a) for persons with mild to moderately severe hearing losses, appropriately adjusted custom ITE fittings typically yield speech intelligibility that is equal to the better OTE fitting identified in a comparative evaluation; and (b) gain prescriptions for ITE hearing aids should be adjusted to account for the high-frequency emphasis associated with in-the-concha microphone placement.

Hearing Aid Design and Evaluation for a Patient with a Severe Discrimination Loss for Speech

1967 ◽  
Vol 10 (2) ◽  
pp. 367-372 ◽  
Author(s):  
James D. Miller ◽  
Arthur F. Niemoeller
Keyword(s):  
Hearing Aids ◽  
Background Noise ◽  
Hearing Aid ◽  
Accurate Estimate ◽  
Single Patient

Results of intelligibility tests on a single patient with a severe discrimination loss for speech are reported. The patient was tested with four different hearing aids and with no aid, and the effects of opportunity for lipreading, background noise, and reverberation were evaluated. The tests appear to allow an accurate estimate of the amount of help to be expected in various situations and show that an aid with good fidelity is clearly superior to the others tested. The destructive effects of background noise and reverberation are demonstrated separately and in combination.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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.

scholarly journals Integrated Bimodal Fitting for Unilateral CI Users with Residual Contralateral Hearing

2021 ◽  
Vol 11 (2) ◽  
pp. 200-206
Author(s):  
Gennaro Auletta ◽  
Annamaria Franzè ◽  
Carla Laria ◽  
Carmine Piccolo ◽  
Carmine Papa ◽  
...  
Keyword(s):  
Cochlear Implants ◽  
Hearing Aids ◽  
Speech Intelligibility ◽  
Hearing Aid ◽  
Performance Difference ◽  
Test Conditions ◽  
Significant Difference ◽  
Noisy Conditions ◽  
New Formula ◽  
Better Than

Background: The aim of this study was to compare, in users of bimodal cochlear implants, the performance obtained using their own hearing aids (adjusted with the standard NAL-NL1 fitting formula) with the performance using the Phonak Naìda Link Ultra Power hearing aid adjusted with both NAL-NL1 and a new bimodal system (Adaptive Phonak Digital Bimodal (APDB)) developed by Advanced Bionics and Phonak Corporations. Methods: Eleven bimodal users (Naìda CI Q70 + contralateral hearing aid) were enrolled in our study. The users’ own hearing aids were replaced with the Phonak Naìda Link Ultra Power and fitted following the new formula. Speech intelligibility was assessed in quiet and noisy conditions, and comparisons were made with the results obtained with the users’ previous hearing aids and with the Naída Link hearing aids fitted with the NAL-NL1 generic prescription formula. Results: Using Phonak Naìda Link Ultra Power hearing aids with the Adaptive Phonak Digital Bimodal fitting formula, performance was significantly better than that with the users’ own rehabilitation systems, especially in challenging hearing situations for all analyzed subjects. Conclusions: Speech intelligibility tests in quiet settings did not reveal a significant difference in performance between the new fitting formula and NAL-NL1 fittings (using the Naída Link hearing aids), whereas the performance difference between the two fittings was very significant in noisy test conditions.

Artificial Intelligence, Machine Learning and Calculation of Intraocular Lens Power

2020 ◽  
Vol 237 (12) ◽  
pp. 1430-1437
Author(s):  
Achim Langenbucher ◽  
Nóra Szentmáry ◽  
Jascha Wendelstein ◽  
Peter Hoffmann
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Regression Model ◽  
Prediction Error ◽  
Learning Algorithms ◽  
Anterior Chamber Depth ◽  
Gaussian Process Regression ◽  
Machine Learning Algorithms ◽  
Effect Sizes ◽  
Axial Position

Abstract Background and Purpose In the last decade, artificial intelligence and machine learning algorithms have been more and more established for the screening and detection of diseases and pathologies, as well as for describing interactions between measures where classical methods are too complex or fail. The purpose of this paper is to model the measured postoperative position of an intraocular lens implant after cataract surgery, based on preoperatively assessed biometric effect sizes using techniques of machine learning. Patients and Methods In this study, we enrolled 249 eyes of patients who underwent elective cataract surgery at Augenklinik Castrop-Rauxel. Eyes were measured preoperatively with the IOLMaster 700 (Carl Zeiss Meditec), as well as preoperatively and postoperatively with the Casia 2 OCT (Tomey). Based on preoperative effect sizes axial length, corneal thickness, internal anterior chamber depth, thickness of the crystalline lens, mean corneal radius and corneal diameter a selection of 17 machine learning algorithms were tested for prediction performance for calculation of internal anterior chamber depth (AQD_post) and axial position of equatorial plane of the lens in the pseudophakic eye (LEQ_post). Results The 17 machine learning algorithms (out of 4 families) varied in root mean squared/mean absolute prediction error between 0.187/0.139 mm and 0.255/0.204 mm (AQD_post) and 0.183/0.135 mm and 0.253/0.206 mm (LEQ_post), using 5-fold cross validation techniques. The Gaussian Process Regression Model using an exponential kernel showed the best performance in terms of root mean squared error for prediction of AQDpost and LEQpost. If the entire dataset is used (without splitting for training and validation data), comparison of a simple multivariate linear regression model vs. the algorithm with the best performance showed a root mean squared prediction error for AQD_post/LEQ_post with 0.188/0.187 mm vs. the best performance Gaussian Process Regression Model with 0.166/0.159 mm. Conclusion In this paper we wanted to show the principles of supervised machine learning applied to prediction of the measured physical postoperative axial position of the intraocular lenses. Based on our limited data pool and the algorithms used in our setting, the benefit of machine learning algorithms seems to be limited compared to a standard multivariate regression model.

scholarly journals mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology

mSphere ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Artur Yakimovich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Edge Label ◽  
Bacterial Colony ◽  
Link Type ◽  
Colony Counting ◽  
Anthrax Spores ◽  
Infection Biology

ABSTRACT Artur Yakimovich works in the field of computational virology and applies machine learning algorithms to study host-pathogen interactions. In this mSphere of Influence article, he reflects on two papers “Holographic Deep Learning for Rapid Optical Screening of Anthrax Spores” by Jo et al. (Y. Jo, S. Park, J. Jung, J. Yoon, et al., Sci Adv 3:e1700606, 2017, https://doi.org/10.1126/sciadv.1700606) and “Bacterial Colony Counting with Convolutional Neural Networks in Digital Microbiology Imaging” by Ferrari and colleagues (A. Ferrari, S. Lombardi, and A. Signoroni, Pattern Recognition 61:629–640, 2017, https://doi.org/10.1016/j.patcog.2016.07.016). Here he discusses how these papers made an impact on him by showcasing that artificial intelligence algorithms can be equally applicable to both classical infection biology techniques and cutting-edge label-free imaging of pathogens.

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