Effects of signal‐to‐noise ratio on the frequency discrimination of complex tones with overlapping and nonoverlapping harmonics

1991 ◽  
Vol 89 (4B) ◽  
pp. 1888-1888
Author(s):  
Brian C. J. Moore ◽  
Brian R. Glasberg
Keyword(s):  
Signal To Noise Ratio ◽  
Frequency Discrimination ◽  
Signal To Noise ◽  
Complex Tones ◽  
Noise Ratio

scholarly journals A Temporal Model of Aural Frequency Discrimination

2021 ◽  
Author(s):  
◽  
Kaye McAulay
Keyword(s):  
Signal To Noise Ratio ◽  
Discrimination Performance ◽  
Frequency Discrimination ◽  
Signal To Noise ◽  
Low Frequencies ◽  
Human Data ◽  
Temporal Intervals ◽  
Temporal Model ◽  
Noise Ratio ◽  
Positive Axis

<p>The importance of temporal information versus place information in frequency analysis by the ear is a continuing controversy. This dissertation developes a temporal model which simulates human frequency discrimination. The model gives guantitative measures of performance for the discrimination of sinusoids in white gaussian noise. The model simulates human frequency discrimination performance as a function of frequency and signal-to-noise ratio. The model's predictions are based on the temporal intervals between the positive axis crossings of the stimulus. The histograms of these temporal intervals were used as the underlying distributions from which indices of discriminability were calculated. Human freguency discrimination data was obtained for five observers as a function of frequency and signal-to-noise ratio. The data were analysed using the method of Group-operating-characteristic (GOC) Analysis. This method of analysis statistically removes unique noise from data. The unique noise was removed by summing observers' ratings for identical stimuli. This method of analysis gave human frequency discrimination data with less unigue noise than any existing frequency data. The human data were used for evaluating the model. The GOC Analysis was also used to study the improvement in d' as a function of stimulus replications and signal-to-noise ratio. The model was a good fit to the human data at 250 Hz, for two signal-to-noise ratios. The model did not fit the data at 1000 Hz or 5000 Hz. There was some evidence of a transition occuring at 1000 Hz. This investigation supported the idea that human frequency discrimination relies on a temporal mechanism at low frequencies with a transition to some other mechanism at about lO00 Hz.</p>

scholarly journals A Temporal Model of Aural Frequency Discrimination

2021 ◽  
Author(s):  
◽  
Kaye McAulay
Keyword(s):  
Signal To Noise Ratio ◽  
Discrimination Performance ◽  
Frequency Discrimination ◽  
Signal To Noise ◽  
Low Frequencies ◽  
Human Data ◽  
Temporal Intervals ◽  
Temporal Model ◽  
Noise Ratio ◽  
Positive Axis

<p>The importance of temporal information versus place information in frequency analysis by the ear is a continuing controversy. This dissertation developes a temporal model which simulates human frequency discrimination. The model gives guantitative measures of performance for the discrimination of sinusoids in white gaussian noise. The model simulates human frequency discrimination performance as a function of frequency and signal-to-noise ratio. The model's predictions are based on the temporal intervals between the positive axis crossings of the stimulus. The histograms of these temporal intervals were used as the underlying distributions from which indices of discriminability were calculated. Human freguency discrimination data was obtained for five observers as a function of frequency and signal-to-noise ratio. The data were analysed using the method of Group-operating-characteristic (GOC) Analysis. This method of analysis statistically removes unique noise from data. The unique noise was removed by summing observers' ratings for identical stimuli. This method of analysis gave human frequency discrimination data with less unigue noise than any existing frequency data. The human data were used for evaluating the model. The GOC Analysis was also used to study the improvement in d' as a function of stimulus replications and signal-to-noise ratio. The model was a good fit to the human data at 250 Hz, for two signal-to-noise ratios. The model did not fit the data at 1000 Hz or 5000 Hz. There was some evidence of a transition occuring at 1000 Hz. This investigation supported the idea that human frequency discrimination relies on a temporal mechanism at low frequencies with a transition to some other mechanism at about lO00 Hz.</p>

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

Speech Reception in the Presence of Classroom Noise

1979 ◽  
Vol 10 (4) ◽  
pp. 221-230 ◽  
Author(s):  
Veronica Smyth
Keyword(s):  
Signal To Noise Ratio ◽  
Learning Processes ◽  
Speech Discrimination ◽  
Signal To Noise ◽  
Speech Reception ◽  
Monosyllabic Words ◽  
Noise Ratio

Three hundred children from five to 12 years of age were required to discriminate simple, familiar, monosyllabic words under two conditions: 1) quiet, and 2) in the presence of background classroom noise. Of the sample, 45.3% made errors in speech discrimination in the presence of background classroom noise. The effect was most marked in children younger than seven years six months. The results are discussed considering the signal-to-noise ratio and the possible effects of unwanted classroom noise on learning processes.

Quantifying the Effects of Background Speech Babble on Preschool Children's Novel Word Learning in a Multisession Paradigm: A Preliminary Study

2020 ◽  
Vol 63 (1) ◽  
pp. 345-356
Author(s):  
Meital Avivi-Reich ◽  
Megan Y. Roberts ◽  
Tina M. Grieco-Calub
Keyword(s):  
Word Learning ◽  
Signal To Noise Ratio ◽  
Preschool Age ◽  
Exposure Condition ◽  
Signal To Noise ◽  
Verbal Recall ◽  
Referent Selection ◽  
Main Effect ◽  
Noise Ratio ◽  
Novel Word Learning

Purpose This study tested the effects of background speech babble on novel word learning in preschool children with a multisession paradigm. Method Eight 3-year-old children were exposed to a total of 8 novel word–object pairs across 2 story books presented digitally. Each story contained 4 novel consonant–vowel–consonant nonwords. Children were exposed to both stories, one in quiet and one in the presence of 4-talker babble presented at 0-dB signal-to-noise ratio. After each story, children's learning was tested with a referent selection task and a verbal recall (naming) task. Children were exposed to and tested on the novel word–object pairs on 5 separate days within a 2-week span. Results A significant main effect of session was found for both referent selection and verbal recall. There was also a significant main effect of exposure condition on referent selection performance, with more referents correctly selected for word–object pairs that were presented in quiet compared to pairs presented in speech babble. Finally, children's verbal recall of novel words was statistically better than baseline performance (i.e., 0%) on Sessions 3–5 for words exposed in quiet, but only on Session 5 for words exposed in speech babble. Conclusions These findings suggest that background speech babble at 0-dB signal-to-noise ratio disrupts novel word learning in preschool-age children. As a result, children may need more time and more exposures of a novel word before they can recognize or verbally recall it.

On the Signal-to-Noise Ratio for Boolean Functions

2020 ◽  
Vol E103.A (12) ◽  
pp. 1659-1665
Author(s):  
Yu ZHOU ◽  
Wei ZHAO ◽  
Zhixiong CHEN ◽  
Weiqiong WANG ◽  
Xiaoni DU
Keyword(s):  
Boolean Functions ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio

An Over 120dB Dynamic Range Linear Response Single Exposure CMOS Image Sensor with Two-stage Lateral Overflow Integration Trench Capacitors

2020 ◽  
Vol 2020 (7) ◽  
pp. 143-1-143-6 ◽  
Author(s):  
Yasuyuki Fujihara ◽  
Maasa Murata ◽  
Shota Nakayama ◽  
Rihito Kuroda ◽  
Shigetoshi Sugawa
Keyword(s):  
Linear Response ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Signal To Noise ◽  
Single Exposure ◽  
Two Stage ◽  
Noise Ratio ◽  
Maximum Signal

This paper presents a prototype linear response single exposure CMOS image sensor with two-stage lateral overflow integration trench capacitors (LOFITreCs) exhibiting over 120dB dynamic range with 11.4Me- full well capacity (FWC) and maximum signal-to-noise ratio (SNR) of 70dB. The measured SNR at all switching points were over 35dB thanks to the proposed two-stage LOFITreCs.

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