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

2006 ◽  
Vol 120 (5) ◽  
pp. 3158-3158
Author(s):  
Birger Kollmeier ◽  
Rainer Huber ◽  
Thomas Rohdenburg ◽  
Volker Hohmann
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Sound Quality ◽  
Quality Prediction ◽  
Model Based ◽  
Perception Model

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.

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.

Evaluation of model-based versus non-parametric monaural noise-reduction approaches for hearing aids

2012 ◽  
Vol 51 (8) ◽  
pp. 627-639 ◽  
Author(s):  
Niklas Harlander ◽  
Tobias Rosenkranz ◽  
Volker Hohmann
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Model Based ◽  
Non Parametric

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

2008 ◽  
Vol 123 (5) ◽  
pp. 3166-3166
Author(s):  
Birger Kollmeier ◽  
Rainer Huber ◽  
Thomas Rohdenburg ◽  
Rainer Beutelmann ◽  
Volker Hohmann
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Objective Assessment ◽  
Model Based

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 Speech Perception and Sound-Quality Rating with an Adaptive Nonlinear Frequency Compression Algorithm in Mandarin-Speaking Hearing Aid Users

2020 ◽  
Vol 31 (08) ◽  
pp. 590-598
Author(s):  
Li Xu ◽  
Solveig C. Voss ◽  
Jing Yang ◽  
Xianhui Wang ◽  
Qian Lu ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Hearing Aids ◽  
Detection Threshold ◽  
Sound Quality ◽  
Hearing Impaired ◽  
Nonlinear Frequency ◽  
Frequency Compression ◽  
Speech Reception ◽  
Quality Ratings ◽  
Phoneme Detection

Abstract Background Mandarin Chinese has a rich repertoire of high-frequency speech sounds. This may pose a remarkable challenge to hearing-impaired listeners who speak Mandarin Chinese because of their high-frequency sloping hearing loss. An adaptive nonlinear frequency compression (adaptive NLFC) algorithm has been implemented in contemporary hearing aids to alleviate the problem. Purpose The present study examined the performance of speech perception and sound-quality rating in Mandarin-speaking hearing-impaired listeners using hearing aids fitted with adaptive NLFC (i.e., SoundRecover2 or SR2) at different parameter settings. Research Design Hearing-impaired listeners' phoneme detection thresholds, speech reception thresholds, and sound-quality ratings were collected with various SR2 settings. Study Sample The participants included 15 Mandarin-speaking adults aged 32 to 84 years old who had symmetric sloping severe-to-profound sensorineural hearing loss. Intervention The participants were fitted bilaterally with Phonak Naida V90-SP hearing aids. Data Collection and Analysis The outcome measures included phoneme detection threshold using the Mandarin Phonak Phoneme Perception test, speech reception threshold using the Mandarin hearing in noise test (M-HINT), and sound-quality ratings on human speech in quiet and noise, bird chirps, and music in quiet. For each test, five experimental settings were applied and compared: SR2-off, SR2-weak, SR2-default, SR2-strong 1, and SR2-strong 2. Results The results showed that listeners performed significantly better with SR2-strong 1 and SR2-strong 2 settings than with SR2-off or SR2-weak settings for speech reception threshold and phoneme detection threshold. However, no significant improvement was observed in sound-quality ratings among different settings. Conclusions These preliminary findings suggested that the adaptive NLFC algorithm provides perceptual benefit to Mandarin-speaking people with severe-to-profound hearing loss.

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