Using tactile aids to provide low frequency information for cochlear implant users

2013 ◽  
Vol 134 (5) ◽  
pp. 4235-4235 ◽  
Author(s):  
Shuai Wang ◽  
Xuan Zhong ◽  
Michael F. Dorman ◽  
William A. Yost ◽  
Julie M. Liss
Keyword(s):  
Cochlear Implant ◽  
Low Frequency ◽  
Frequency Information ◽  
Tactile Aids

scholarly journals Effects of Removing Low-Frequency Electric Information on Speech Perception With Bimodal Hearing

2016 ◽  
Vol 59 (1) ◽  
pp. 99-109 ◽  
Author(s):  
Jennifer R. Fowler ◽  
Jessica L. Eggleston ◽  
Kelly M. Reavis ◽  
Garnett P. McMillan ◽  
Lina A. J. Reiss
Keyword(s):  
Speech Perception ◽  
Cochlear Implant ◽  
Hearing Aid ◽  
Cutoff Frequency ◽  
Low Frequency ◽  
Improve Performance ◽  
Frequency Information ◽  
Bimodal Condition ◽  
Contralateral Ear ◽  
Bimodal Hearing

PurposeThe objective was to determine whether speech perception could be improved for bimodal listeners (those using a cochlear implant [CI] in one ear and hearing aid in the contralateral ear) by removing low-frequency information provided by the CI, thereby reducing acoustic–electric overlap.MethodSubjects were adult CI subjects with at least 1 year of CI experience. Nine subjects were evaluated in the CI-only condition (control condition), and 26 subjects were evaluated in the bimodal condition. CIs were programmed with 4 experimental programs in which the low cutoff frequency (LCF) was progressively raised. Speech perception was evaluated using Consonant-Nucleus-Consonant words in quiet, AzBio sentences in background babble, and spondee words in background babble.ResultsThe CI-only group showed decreased speech perception in both quiet and noise as the LCF was raised. Bimodal subjects with better hearing in the hearing aid ear (< 60 dB HL at 250 and 500 Hz) performed best for words in quiet as the LCF was raised. In contrast, bimodal subjects with worse hearing (> 60 dB HL at 250 and 500 Hz) performed similarly to the CI-only group.ConclusionsThese findings suggest that reducing low-frequency overlap of the CI and contralateral hearing aid may improve performance in quiet for some bimodal listeners with better hearing.

scholarly journals Reducing interaural tonotopic mismatch preserves binaural unmasking in cochlear implant simulations of single-sided deafness

2020 ◽  
Author(s):  
Elad Sagi ◽  
Mahan Azadpour ◽  
Jonathan Neukam ◽  
Nicole Hope Capach ◽  
Mario A. Svirsky
Keyword(s):  
Cochlear Implant ◽  
Low Frequency ◽  
Spatial Separation ◽  
Binaural Hearing ◽  
Normal Hearing ◽  
Input Frequency ◽  
Frequency Range ◽  
Speech Processor ◽  
Frequency Information ◽  
Single Sided Deafness

Binaural unmasking, a key feature of normal binaural hearing, refers to the improved intelligibility of masked speech by adding masking noise that facilities perceived spatial separation of target and masker. A question particularly relevant for cochlear implant users with single-sided deafness (SSD-CI) is whether binaural unmasking can still be achieved if the additional masking is distorted. Adding the CI restores some aspects of binaural hearing to these listeners, although binaural unmasking remains limited. Notably, these listeners may experience a mismatch between the frequency information perceived through the CI and that perceived by their normal hearing ear. Employing acoustic simulations of SSD-CI with normal hearing listeners, the present study confirms a previous simulation study that binaural unmasking is severely limited when interaural frequency mismatch between the input frequency range and simulated place of stimulation exceeds 1-2 mm. The present study also shows that binaural unmasking is largely retained when the input frequency range is adjusted to match simulated place of stimulation, even at the expense of removing low-frequency information. This result bears implication for the mechanisms driving the type of binaural unmasking of the present study, as well as for mapping the frequency range of the CI speech processor in SSD-CI users.

Sound quality perception of cochlear implant recipients: low-frequency information and foreign-language effect

2021 ◽  
pp. 1-9
Author(s):  
Niyazi Ömer Arslan ◽  
Ahmet Alperen Akbulut ◽  
Büşra Köse ◽  
Ayşenur Karaman-Demirel ◽  
Ufuk Derinsu
Keyword(s):  
Foreign Language ◽  
Cochlear Implant ◽  
Low Frequency ◽  
Sound Quality ◽  
Quality Perception ◽  
Frequency Information

Lipreading with Auditory Low-frequency Information Contextual Constraints

1996 ◽  
Vol 25 (2) ◽  
pp. 127-132
Author(s):  
Jerker Rönnberg ◽  
Stefan Samuelsson ◽  
Björn Lyxell ◽  
Stig Arlinger
Keyword(s):  
Low Frequency ◽  
Frequency Information

scholarly journals Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR

2021 ◽  
Vol 11 (11) ◽  
pp. 5028
Author(s):  
Miaomiao Sun ◽  
Zhenchun Li ◽  
Yanli Liu ◽  
Jiao Wang ◽  
Yufei Su
Keyword(s):  
Reflection Coefficient ◽  
Compressed Sensing ◽  
Objective Function ◽  
Low Frequency ◽  
Original Data ◽  
Seismic Exploration ◽  
High Signal ◽  
Inversion Process ◽  
Frequency Information ◽  
Low Snr

Low-frequency information can reflect the basic trend of a formation, enhance the accuracy of velocity analysis and improve the imaging accuracy of deep structures in seismic exploration. However, the low-frequency information obtained by the conventional seismic acquisition method is seriously polluted by noise, which will be further lost in processing. Compressed sensing (CS) theory is used to exploit the sparsity of the reflection coefficient in the frequency domain to expand the low-frequency components reasonably, thus improving the data quality. However, the conventional CS method is greatly affected by noise, and the effective expansion of low-frequency information can only be realized in the case of a high signal-to-noise ratio (SNR). In this paper, well information is introduced into the objective function to constrain the inversion process of the estimated reflection coefficient, and then, the low-frequency component of the original data is expanded by extracting the low-frequency information of the reflection coefficient. It has been proved by model tests and actual data processing results that the objective function of estimating the reflection coefficient constrained by well logging data based on CS theory can improve the anti-noise interference ability of the inversion process and expand the low-frequency information well in the case of a low SNR.

Research on Improvement Algorithm of Image Edge Detection

2014 ◽  
Vol 539 ◽  
pp. 141-145
Author(s):  
Shui Li Zhang
Keyword(s):  
Cognitive Psychology ◽  
Edge Detection ◽  
High Frequency ◽  
Detection Method ◽  
Low Frequency ◽  
Frequency Information ◽  
Image Edge Detection ◽  
Improvement Algorithm ◽  
Image Edge ◽  
Edge Detection Method

This paper presents new theorems Stevens edge detection method based on cognitive psychology on. Firstly, based on the number of the image is decomposed into high-frequency and low-frequency information, and the high-frequency information extracted by subtracting the maximum number of images to the image after the filter, then the amount of high frequency information into psychological cognitive psychology based on Stevenss theorem. The algorithm suppression refined edge after the non-minimum, applications Pillar K-means algorithm to extract image edge. Experimental results show that: the brightness of the image is converted to the amount of psychological edge can better unify under different brightness values.

The enhanced deep Plug-and-play super-resolution algorithm with residual channel attention networks

2021 ◽  
pp. 1-10
Author(s):  
Hongguang Pan ◽  
Fan Wen ◽  
Xiangdong Huang ◽  
Xinyu Lei ◽  
Xiaoling Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Super Resolution ◽  
Structural Similarity ◽  
Plug And Play ◽  
Attention Networks ◽  
Frequency Information ◽  
Blind Deblurring ◽  
Adaptive Adjustment ◽  
Deep Feature Extraction

In the field of super-resolution image reconstruction, as a learning-based method, deep plug-and-play super-resolution (DPSR) algorithm can be used to find the blur kernel by using the existing blind deblurring methods. However, DPSR is not flexible enough in processing images with high- and low-frequency information. Considering a channel attention mechanism can distinguish low-frequency information and features in low-resolution images, in this paper, we firstly introduce this mechanism and design a new residual channel attention networks (RCAN); then the RCAN is adopted to replace deep feature extraction part in DPSR to achieve the adaptive adjustment of channel characteristics. Through four test experiments based on Set5, Set14, Urban100 and BSD100 datasets, we find that, under different blur kernels and different scale factors, the average peak signal to noise ratio (PSNR) and structural similarity (SSIM) values of our proposed method increase by 0.31dB and 0.55%, respectively; under different noise levels, the average PSNR and SSIM values increase by 0.26dB and 0.51%, respectively.

Research on Adaptive Face Gender Recognition Based on Compressive Sensing

2014 ◽  
Vol 989-994 ◽  
pp. 4187-4190 ◽  
Author(s):  
Lin Zhang
Keyword(s):  
Feature Extraction ◽  
Compressive Sensing ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Low Frequency ◽  
Face Image ◽  
Gender Recognition ◽  
Recognition Method ◽  
Frequency Information ◽  
Learning Machine

An adaptive gender recognition method is proposed in this paper. At first, do multiwavlet transform to face image and get its low frequency information, then do feature extraction to the low frequency information using compressive sensing (CS), use extreme learning machine (ELM) to achieve gender recognition finally. In the process of feature extraction, we use genetic algorithm (GA) to get the number of measurements of CS in order to gain the highest recognition rate, so the method can adaptive access optimal performance. Experimental results show that compared with PDA and LDA, the new method improved the recognition accuracy substantially.

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