scholarly journals Performance of an adaptive beamforming noise reduction scheme for hearing aid applications. II. Experimental verification of the predictions

2001 ◽  
Vol 109 (3) ◽  
pp. 1134-1143 ◽  
Author(s):  
Martin Kompis ◽  
Norbert Dillier
Keyword(s):  
Noise Reduction ◽  
Experimental Verification ◽  
Hearing Aid ◽  
Adaptive Beamforming ◽  
Reduction Scheme

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 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 Evaluation of a real‐time adaptive‐beamforming hearing aid

1989 ◽  
Vol 86 (S1) ◽  
pp. S87-S88 ◽  
Author(s):  
Julie E. Greenberg ◽  
Patrick M. Zurek
Keyword(s):  
Real Time ◽  
Hearing Aid ◽  
Adaptive Beamforming

scholarly journals An efficient Scheme to Remove Low Density Impulse Noise From A Digital Image

2016 ◽  
Vol 15 (12) ◽  
pp. 7284-7289
Author(s):  
Dr. Jihad N. Abdeljalil Al-Balqa
Keyword(s):  
Noise Reduction ◽  
Impulse Noise ◽  
Low Density ◽  
Salt And Pepper Noise ◽  
Reduction Scheme ◽  
Iir Filter ◽  
Noise Impulse ◽  
Efficient Scheme ◽  
Adaptive Noise ◽  
Salt And Pepper

An improved adaptive noise reduction scheme for images that are highly corrupted by Salt-and-Pepper noise(impulse noise), is proposed in this paper which efficiently removes the salt and pepper noise while preserving the details. The proposed scheme efficiently identifies and reduces salt and pepper noise. The algorithm utilizes an IIR filter with controlled parameters to get better image quality than the existing methods of noise removing. A comparative analysis between the proposed scheme and other techniques of noise reduction or removing is presented in order to show the advantages of the proposed scheme in removing the noisy pixels and producing a better PSNR.

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.

ENHANCEMENT OF MAMMOGRAPHIC PHANTOM FEATURES BY NOISE REDUCTION

2007 ◽  
Vol 21 (06) ◽  
pp. 1047-1057
Author(s):  
MOULOUD ADEL ◽  
DANIEL ZUWALA ◽  
MONIQUE RASIGNI ◽  
SALAH BOURENNANE
Keyword(s):  
Noise Reduction ◽  
Mean Squared Error ◽  
Point Of View ◽  
Reduction Scheme ◽  
Squared Error ◽  
Modification Method ◽  
Optimal Function ◽  
Direct Contrast ◽  
The Mean ◽  
Simulated Images

A noise reduction scheme on digitized mammographic phantom images is presented. This algorithm is based on a direct contrast modification method with an optimal function, obtained by using the mean squared error as a criterion. Computer simulated images containing objects similar to those observed in the phantom are built to evaluate the performance of the algorithm. Noise reduction results obtained on both simulated and real phantom images show that the developed method may be considered as a good preprocessing step from the point of view of automating phantom film evaluation by means of image processing.

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