scholarly journals CLCNET: Deep Learning-Based Noise Reduction for Hearing aids using Complex Linear Coding

2020 ◽  
Author(s):  
H. Schroter ◽  
T. Rosenkranz ◽  
A. N. Escalante-B ◽  
M. Aubreville ◽  
A. Maier
Keyword(s):  
Deep Learning ◽  
Noise Reduction ◽  
Hearing Aids ◽  
Complex Linear

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 Measuring the Influence of Noise Reduction on Listening Effort in Hearing-Impaired Listeners Using Response Times to an Arithmetic Task in Noise

2021 ◽  
Vol 25 ◽  
pp. 233121652110144
Author(s):  
Ilja Reinten ◽  
Inge De Ronde-Brons ◽  
Rolph Houben ◽  
Wouter Dreschler
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Speech Intelligibility ◽  
Response Times ◽  
Hearing Impaired ◽  
Listening Effort ◽  
Arithmetic Task ◽  
Test Retest Reliability ◽  
Influence Of Noise ◽  
Measured Response

Single microphone noise reduction (NR) in hearing aids can provide a subjective benefit even when there is no objective improvement in speech intelligibility. A possible explanation lies in a reduction of listening effort. Previously, we showed that response times (a proxy for listening effort) to an auditory-only dual-task were reduced by NR in normal-hearing (NH) listeners. In this study, we investigate if the results from NH listeners extend to the hearing-impaired (HI), the target group for hearing aids. In addition, we assess the relevance of the outcome measure for studying and understanding listening effort. Twelve HI subjects were asked to sum two digits of a digit triplet in noise. We measured response times to this task, as well as subjective listening effort and speech intelligibility. Stimuli were presented at three signal-to-noise ratios (SNR; –5, 0, +5 dB) and in quiet. Stimuli were processed with ideal or nonideal NR, or unprocessed. The effect of NR on response times in HI listeners was significant only in conditions where speech intelligibility was also affected (–5 dB SNR). This is in contrast to the previous results with NH listeners. There was a significant effect of SNR on response times for HI listeners. The response time measure was reasonably correlated ( R142 = 0.54) to subjective listening effort and showed a sufficient test–retest reliability. This study thus presents an objective, valid, and reliable measure for evaluating an aspect of listening effort of HI listeners.

scholarly journals Noise reduction in gravitational-wave data via deep learning

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Rich Ormiston ◽  
Tri Nguyen ◽  
Michael Coughlin ◽  
Rana X. Adhikari ◽  
Erik Katsavounidis
Keyword(s):  
Deep Learning ◽  
Gravitational Wave ◽  
Noise Reduction ◽  
Wave Data

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.

scholarly journals Multitasking with typical use of hearing aid noise reduction in older listeners

2018 ◽  
Author(s):  
Tim Schoof ◽  
Pamela Souza
Keyword(s):  
Speech Recognition ◽  
Noise Reduction ◽  
Hearing Aids ◽  
Recognition Task ◽  
Hearing Impaired ◽  
Improve Performance ◽  
Sentence Recognition ◽  
Monitoring Task ◽  
Speech In Noise ◽  
Dual Task Paradigm

Objective: Older hearing-impaired adults typically experience difficulties understanding speech in noise. Most hearing aids address this issue using digital noise reduction. While noise reduction does not necessarily improve speech recognition, it may reduce the resources required to process the speech signal. Those available resources may, in turn, aid the ability to perform another task while listening to speech (i.e., multitasking). This study examined to what extent changing the strength of digital noise reduction in hearing aids affects the ability to multitask. Design: Multitasking was measured using a dual-task paradigm, combining a speech recognition task and a visual monitoring task. The speech recognition task involved sentence recognition in the presence of six-talker babble at signal-to-noise ratios (SNRs) of 2 and 7 dB. Participants were fit with commercially-available hearing aids programmed under three noise reduction settings: off, mild, strong. Study sample: 18 hearing-impaired older adults. Results: There were no effects of noise reduction on the ability to multitask, or on the ability to recognize speech in noise. Conclusions: Adjustment of noise reduction settings in the clinic may not invariably improve performance for some tasks.

scholarly journals Effect of Digital Noise Reduction of Hearing Aids on Music and Speech Perception

2020 ◽  
Vol 24 (4) ◽  
pp. 180-190
Author(s):  
Hyo Jeong Kim ◽  
Jae Hee Lee ◽  
Hyun Joon Shim
Keyword(s):  
Speech Perception ◽  
Noise Reduction ◽  
Hearing Aids ◽  
Music Perception ◽  
Objective Assessment ◽  
Subjective Assessment ◽  
Pitch Discrimination ◽  
Korean Music ◽  
Sentence Recognition ◽  
Assessment Tests

Background and Objectives: Although many studies have evaluated the effect of the digital noise reduction (DNR) algorithm of hearing aids (HAs) on speech recognition, there are few studies on the effect of DNR on music perception. Therefore, we aimed to evaluate the effect of DNR on music, in addition to speech perception, using objective and subjective measurements. Subjects and Methods: Sixteen HA users participated in this study (58.00±10.44 years; 3 males and 13 females). The objective assessment of speech and music perception was based on the Korean version of the Clinical Assessment of Music Perception test and word and sentence recognition scores. Meanwhile, for the subjective assessment, the quality rating of speech and music as well as self-reported HA benefits were evaluated. Results: There was no improvement conferred with DNR of HAs on the objective assessment tests of speech and music perception. The pitch discrimination at 262 Hz in the DNR-off condition was better than that in the unaided condition (<i>p</i>=0.024); however, the unaided condition and the DNR-on conditions did not differ. In the Korean music background questionnaire, responses regarding ease of communication were better in the DNR-on condition than in the DNR-off condition (<i>p</i>=0.029). Conclusions: Speech and music perception or sound quality did not improve with the activation of DNR. However, DNR positively influenced the listener’s subjective listening comfort. The DNR-off condition in HAs may be beneficial for pitch discrimination at some frequencies.

Effectiveness of Directional Microphones and Noise Reduction Schemes in Hearing Aids: A Systematic Review of the Evidence

2005 ◽  
Vol 16 (07) ◽  
pp. 473-484 ◽  
Author(s):  
Ruth A. Bentler
Keyword(s):  
Systematic Review ◽  
Noise Reduction ◽  
Hearing Aids ◽  
Small Sample Size ◽  
Careful Consideration ◽  
Small Sample ◽  
Experimental Conditions ◽  
Directional Microphones ◽  
Directional Microphone

A systematic review of the literature was undertaken to find evidence of real-world effectiveness of directional microphone and digital noise reduction features in current hearing aids. The evidence was drawn from randomized controlled trials, nonrandomized intervention studies, and descriptive studies. The quality of each study was evaluated for factors such as blinding, power of statistical analyses, and use of psychometrically strong outcome measures. Weaknesses in the identified studies included small sample size, resultant poor power to detect potentially worthwhile differences, and overlapping experimental conditions. Nine studies were identified for directional microphones, and the evidence (albeit weak) supports effectiveness. Two studies were identified for the noise reduction feature, and the evidence was equivocal. For the researcher, such a systematic review should encourage the careful consideration of appropriate methodologies for assessing feature effectiveness. For the clinician, the outcomes reported herein should encourage use of such a systematic review to drive clinical practice.

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.

Sign in / Sign up

Export Citation Format

Share Document