scholarly journals Mastication noise reduction method for fully implantable hearing aid using piezo-electric sensor

2017 ◽  
Vol 25 ◽  
pp. 29-34 ◽  
Author(s):  
Sung Dae Na ◽  
Gihyoun Lee ◽  
Qun Wei ◽  
Ki Woong Seong ◽  
Jin Ho Cho ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Reduction Method ◽  
Hearing Aid ◽  
Implantable Hearing Aid ◽  
Piezo Electric

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.

Long-term results with the Rion E-type semi-implantable hearing aid

2010 ◽  
Vol 143 (3) ◽  
pp. 422-428 ◽  
Author(s):  
Masahiro Komori ◽  
Naoaki Yanagihara ◽  
Yasuyuki Hinohira ◽  
Naohito Hato ◽  
Kiyofumi Gyo
Keyword(s):  
Hearing Aid ◽  
Long Term Results ◽  
Implantable Hearing Aid

Noise Reduction Analysis of Electronic Device Cooling Fan With Duct and Its Application Under Variable Working Conditions

2021 ◽  
Author(s):  
Zonghan Sun ◽  
Jie Tian ◽  
Grzegorz Liśkiewicz ◽  
Zhaohui Du ◽  
Hua Ouyang
Keyword(s):  
Noise Reduction ◽  
Working Conditions ◽  
Sound Pressure Level ◽  
Sound Pressure ◽  
Reduction Method ◽  
Axial Flow ◽  
Pressure Level ◽  
Inlet Flow ◽  
Cooling Fan ◽  
Inlet Duct

Abstract A noise reduction method for axial flow fans using a short inlet duct is proposed. The pattern of noise reduction imposed by the short inlet duct on the axial flow cooling fan under variable working conditions was experimentally and numerically examined. A 2-cm inlet duct was found to reduce tonal noise. As the tip Mach number of the fan increased from 0.049 to 0.156, the reduction in the total average sound pressure level at 1 m from the fan increased from 0.8 dB(A) to 4.3 dB(A), and further achieved 4.8 dB(A) when a 1-cm inlet duct was used. The steady computational fluid dynamics (CFD) showed that the inlet duct has little effect on the aerodynamic performance of the fan. The results of the full passage unsteady calculation at the maximum flow rate showed that the duct has a significant influence on the suction vortexes caused by the inlet flow non-uniformity. The suction vortexes move upstream to weaken the interaction with the rotor blades, which significantly reduces the pulsating pressure on the blades. The sound pressure level (SPL) at the blade passing frequency (BPF) contributed by the thrust force was calculated to reduce by 36 dB at a 135° observer angle, reflecting the rectification effect of the duct on the non-uniform inlet flow and the improvement in characteristics of the noise source. The proper orthogonal decomposition (POD) of the static pressure field on the blades verified that the main spatial mode is more uniformly distributed due to the duct, and energy owing to the rotor-inlet interaction decreases. A speed regulation strategy for the cooling fan with short inlet duct is proposed, which provides guidance for the application of this noise reduction method.

scholarly journals NOISE REDUCTION METHOD FOR OCT IMAGES BASED ON EMPIRICAL MODE DECOMPOSITION

2013 ◽  
Vol 06 (02) ◽  
pp. 1350009 ◽  
Author(s):  
OLEG O. MYAKININ ◽  
DMITRY V. KORNILIN ◽  
IVAN A. BRATCHENKO ◽  
VALERIY P. ZAKHAROV ◽  
ALEXANDER G. KHRAMOV
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Noise Suppression ◽  
Reduction Method ◽  
Tissue Structure ◽  
Diagnostic Process ◽  
Important Process ◽  
Post Processing ◽  
Mode Decomposition ◽  
Mesh Implant

In this paper, the new method for OCT images denoizing based on empirical mode decomposition (EMD) is proposed. The noise reduction is a very important process for following operations to analyze and recognition of tissue structure. Our method does not require any additional operations and hardware modifications. The basics of proposed method is described. Quality improvement of noise suppression on example of edge-detection procedure using the classical Canny's algorithm without any additional pre- and post-processing operations is demonstrated. Improvement of raw-segmentation in the automatic diagnostic process between a tissue and a mesh implant is shown.

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.

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