Background Noise May Hurt Toddlers’ Ability to Learn Words

ASHA Leader ◽  
2016 ◽  
Vol 21 (10) ◽  
pp. 12-12
Keyword(s):  
Background Noise

Microdensitometer—computer correlation analysis of ultrastructural periodicity

1978 ◽  
Vol 36 (2) ◽  
pp. 32-33
Author(s):  
D.R. Ensor ◽  
C.G. Jensen ◽  
J.A. Fillery ◽  
R.J.K. Baker
Keyword(s):  
Correlation Analysis ◽  
Background Noise ◽  
Computer Control ◽  
Optical Diffraction ◽  
Screw Thread ◽  
Straight Line ◽  
Computer Storage ◽  
Correlation Process ◽  
Electron Microscope Image ◽  
Fixed Beam

Because periodicity is a major indicator of structural organisation numerous methods have been devised to demonstrate periodicity masked by background “noise” in the electron microscope image (e.g. photographic image reinforcement, Markham et al, 1964; optical diffraction techniques, Horne, 1977; McIntosh,1974). Computer correlation analysis of a densitometer tracing provides another means of minimising "noise". The correlation process uncovers periodic information by cancelling random elements. The technique is easily executed, the results are readily interpreted and the computer removes tedium, lends accuracy and assists in impartiality.A scanning densitometer was adapted to allow computer control of the scan and to give direct computer storage of the data. A photographic transparency of the image to be scanned is mounted on a stage coupled directly to an accurate screw thread driven by a stepping motor. The stage is moved so that the fixed beam of the densitometer (which is directed normal to the transparency) traces a straight line along the structure of interest in the image.

Use of a Mild-Gain Hearing Aid by Middle-Age Normal-Hearing Adults Who Do and Do Not Self-Report Trouble Hearing in Background Noise

2020 ◽  
Vol 29 (3) ◽  
pp. 419-428
Author(s):  
Jasleen Singh ◽  
Karen A. Doherty
Keyword(s):  
Hearing Aids ◽  
Background Noise ◽  
Middle Age ◽  
Hearing Aid ◽  
Normal Hearing ◽  
Self Report ◽  
Hearing Aid Use ◽  
Hearing Handicap ◽  
Before And After ◽  
Hearing Problems

Purpose The aim of the study was to assess how the use of a mild-gain hearing aid can affect hearing handicap, motivation, and attitudes toward hearing aids for middle-age, normal-hearing adults who do and do not self-report trouble hearing in background noise. Method A total of 20 participants (45–60 years of age) with clinically normal-hearing thresholds (< 25 dB HL) were enrolled in this study. Ten self-reported difficulty hearing in background noise, and 10 did not self-report difficulty hearing in background noise. All participants were fit with mild-gain hearing aids, bilaterally, and were asked to wear them for 2 weeks. Hearing handicap, attitudes toward hearing aids and hearing loss, and motivation to address hearing problems were evaluated before and after participants wore the hearing aids. Participants were also asked if they would consider purchasing a hearing aid before and after 2 weeks of hearing aid use. Results After wearing the hearing aids for 2 weeks, hearing handicap scores decreased for the participants who self-reported difficulty hearing in background noise. No changes in hearing handicap scores were observed for the participants who did not self-report trouble hearing in background noise. The participants who self-reported difficulty hearing in background noise also reported greater personal distress from their hearing problems, were more motivated to address their hearing problems, and had higher levels of hearing handicap compared to the participants who did not self-report trouble hearing in background noise. Only 20% (2/10) of the participants who self-reported trouble hearing in background noise reported that they would consider purchasing a hearing aid after 2 weeks of hearing aid use. Conclusions The use of mild-gain hearing aids has the potential to reduce hearing handicap for normal-hearing, middle-age adults who self-report difficulty hearing in background noise. However, this may not be the most appropriate treatment option for their current hearing problems given that only 20% of these participants would consider purchasing a hearing aid after wearing hearing aids for 2 weeks.

Selecting the Optimal FM System for Children With Cochlear Implants

2008 ◽  
Vol 18 (1) ◽  
pp. 19-24
Author(s):  
Erin C. Schafer
Keyword(s):  
Speech Recognition ◽  
Cochlear Implants ◽  
Empirical Research ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Evidence Based ◽  
Signal To Noise ◽  
Speech Processor ◽  
System Input ◽  
Optimal Type

Children who use cochlear implants experience significant difficulty hearing speech in the presence of background noise, such as in the classroom. To address these difficulties, audiologists often recommend frequency-modulated (FM) systems for children with cochlear implants. The purpose of this article is to examine current empirical research in the area of FM systems and cochlear implants. Discussion topics will include selecting the optimal type of FM receiver, benefits of binaural FM-system input, importance of DAI receiver-gain settings, and effects of speech-processor programming on speech recognition. FM systems significantly improve the signal-to-noise ratio at the child's ear through the use of three types of FM receivers: mounted speakers, desktop speakers, or direct-audio input (DAI). This discussion will aid audiologists in making evidence-based recommendations for children using cochlear implants and FM systems.

Hearing Aid Design and Evaluation for a Patient with a Severe Discrimination Loss for Speech

1967 ◽  
Vol 10 (2) ◽  
pp. 367-372 ◽  
Author(s):  
James D. Miller ◽  
Arthur F. Niemoeller
Keyword(s):  
Hearing Aids ◽  
Background Noise ◽  
Hearing Aid ◽  
Accurate Estimate ◽  
Single Patient

Results of intelligibility tests on a single patient with a severe discrimination loss for speech are reported. The patient was tested with four different hearing aids and with no aid, and the effects of opportunity for lipreading, background noise, and reverberation were evaluated. The tests appear to allow an accurate estimate of the amount of help to be expected in various situations and show that an aid with good fidelity is clearly superior to the others tested. The destructive effects of background noise and reverberation are demonstrated separately and in combination.

COMPOUND FAULT DIAGNOSIS OF BEARINGS USING AN IMPROVED SPECTRAL KURTOSIS BY MAXIMUM CORRELATION KURTOSIS DECONVOLUTION (MCKD).

2020 ◽  
Vol 9 (8) ◽  
pp. 66-72
Keyword(s):  
Fault Diagnosis ◽  
Resonance Frequency ◽  
Background Noise ◽  
Maximum Correlation ◽  
Vibration Signals ◽  
Filter Length ◽  
Frequency Bands ◽  
Noise Interference ◽  
Spectral Kurtosis

This paper discusses the use of Maximum Correlation kurtosis deconvolution (MCKD) method as a pre-processor in fast spectral kurtosis (FSK) method in order to find the compound fault characteristics of the bearing, by enhancing the vibration signals. FSK only extracts the resonance bands which have maximum kurtosis value, but sometimes it might possible that faults occur in the resonance bands which has low kurtosis value, also the faulty signals missed due to noise interference. In order to overcome these limitations FSK used with MCKD, MCKD extracts various faults present in different resonance frequency bands; also detect the weak impact component, as MCKD also dealt with strong background noise. By obtaining the MCKD parameters like, filter length & deconvolution period, we can extract the compound fault feature characteristics.

Reduction of Acquisition Time for RIL, SDL, and LADA

2007 ◽  
Author(s):  
Arjan Mels ◽  
Frank Zachariasse
Keyword(s):  
Background Noise ◽  
Signal Strength ◽  
Acquisition Time ◽  
Optimal Conditions ◽  
Signal Collection ◽  
Defect Localization ◽  
Model Equations ◽  
Set Up ◽  
Main Operating ◽  
Quantitative Tool

Abstract Although RIL, SDL and LADA are slightly different, the main operating principle is the same and the theory for defect localization presented in this paper is applicable to all three methods. Throughout this paper the authors refer to LADA, as all experimental results in this paper were obtained with a 1064nm laser on defect free circuits. This paper first defines mathematically what 'signal strength' actually means in LADA and then demonstrates a statistical model of the LADA situation that explains the optimal conditions for signal collection and the parameters involved. The model is tested against experimental data and is also used to optimise the acquisition time. Through this model, equations were derived for the acquisition time needed to discern a LADA response from the background noise. The model offers a quantitative tool to estimate the feasibility of a given LADA measurement and a guide to optimising the required experimental set-up.

Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

2020 ◽  
Vol 8 (1) ◽  
pp. 57-73
Author(s):  
Lery Sakti Ramba
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Background Noise ◽  
Electronic Devices ◽  
Recognition System ◽  
Background Intensity ◽  
Automation System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Home Automation System

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.

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