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>

Power spectral analysis of the diaphragm electromyogram

1983 ◽  
Vol 54 (6) ◽  
pp. 1579-1584 ◽  
Author(s):  
T. K. Aldrich ◽  
J. M. Adams ◽  
N. S. Arora ◽  
D. F. Rochester
Keyword(s):  
Power Spectrum ◽  
Signal To Noise Ratio ◽  
Normal Subjects ◽  
Chronic Obstructive ◽  
Signal To Noise ◽  
Low Frequencies ◽  
Mean Values ◽  
The Mean ◽  
Noise Ratio ◽  
Diaphragm Fatigue

We studied the power spectrum of the diaphragm electromyogram (EMG) at frequencies between 31 and 246 Hz in four young normal subjects and five patients with chronic obstructive lung disease (COPD). Diaphragm EMGs were analyzed during spontaneous breathing and maximum inspiratory efforts to determine the effect of signal-to-noise ratio on the power spectrum and if treadmill exercise to dyspnea was associated with diaphragm fatigue. We found that the centroid frequencies of the power spectra (fc) were strongly correlated (r = 0.93) with ratios of power at high frequencies to power at low frequencies (H/L) for all subjects. Of the two indices, H/L had the largest standard deviation expressed as a percentage of the mean. The mean values of both of these decreased significantly after exercise, fc from 100.2 to 97.3 and H/L from 1.07 to 0.97. Signal-to-noise ratios were higher in maximal inspiratory efforts and after exercise in normal subjects and higher in COPD patients. The signal-to-noise ratio was correlated negatively with fc and H/L, indicating that these indices of the shape of the power spectrum are influenced by signal strength and noise levels as well as muscle function. We conclude that the fc and H/L index similar qualities of the power spectrum, that they are partially determined by the signal-to-noise ratio, and that, in some cases, exercise to dyspnea is associated with apparently mild diaphragm fatigue.

Magnetoelectric Sensors With Directly Integrated Charge Sensitive Readout Circuit—Improved Field Sensitivity and Signal-to-Noise Ratio

2011 ◽  
Vol 11 (10) ◽  
pp. 2260-2265 ◽  
Author(s):  
Zhao Fang ◽  
Ninad Mokhariwale ◽  
Feng Li ◽  
Suman Datta ◽  
Q. M. Zhang
Keyword(s):  
Room Temperature ◽  
Signal To Noise Ratio ◽  
Low Noise ◽  
Magnetic Sensors ◽  
Readout Circuit ◽  
Signal To Noise ◽  
Low Frequencies ◽  
Field Sensitivity ◽  
Piezoelectric Layers ◽  
Noise Ratio

The large magnetoelectric (ME) coupling in the ME laminates makes them attractive for ultrasensitive room temperature magnetic sensors. Here ,we investigate the field sensitivity and signal-to-noise ratio (SNR) of ME laminates, consisting of magnetostrictive and piezoelectric layers (Metglas and piezopolymer PVDF were used as the model system), which are directly integrated with a low noise readout circuit. Both the theoretical analysis and experimental results show that increasing the number of piezoelectric layers can improve the SNR, especially at low frequencies. We also introduce a figure of merit to measure the overall influence of the piezolayer properties on the SNR and show that the newly developed piezoelectric single crystals of PMN-PT and PZN-PT have the promise to achieve a very high SNR and consequently ultra-high sensitivity room temperature magnetic sensors. The results show that the ME coefficients used in early ME composites development works may not be relevant to the SNR. The results also show that enhancing the magnetostrictive coefficient, for example, by employing the flux concentration effect, can lead to enhanced SNR.

Low Frequency NQR using Double Contact Cross-relaxation

2000 ◽  
Vol 55 (1-2) ◽  
pp. 37-40
Author(s):  
David Stephenson ◽  
John A. S. Smith
Keyword(s):  
Ammonium Sulphate ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Cross Relaxation ◽  
Relaxation Technique ◽  
Signal To Noise ◽  
Low Frequencies ◽  
Line Shapes ◽  
Ammonium Dichromate ◽  
Noise Ratio

A cross-relaxation technique is described which involves two spin contacts per double reso-nance cycle. The result is an improvement in signal to noise ratio particularly at low frequencies. Experimental spectra and analyses are presented: 14N in ammonium sulphate showing that the tech-nique gives essentially the same information as previous studies; 14N in ammonium dichromate determining e2Qq/h as (76±3) kHz and η = 0.84±.04; 7Li in lithium acetylacetonate for which the spectrum (corrected for Zeeman distortion) yields e2Qq/h = (152 ±5) kHz and η=.5 ±.2. Calculated spectra are presented to demonstrate the η dependence of the line shapes for 7Li.

NQR Studies of Atomic Arrangements and Chemical Bonding in Glasses

1990 ◽  
Vol 45 (3-4) ◽  
pp. 268-272 ◽  
Author(s):  
Donghoon Lee ◽  
S. J. Gravina ◽  
P. J. Bray
Keyword(s):  
Chemical Bonding ◽  
Continuous Wave ◽  
Signal To Noise Ratio ◽  
High Sensitivity ◽  
Signal To Noise ◽  
Low Frequencies ◽  
Large N ◽  
Noise Ratio ◽  
Very High

Abstract A very high sensitivity continuous wave NQR spectrometer was developed to detect pure NQR transitions at low frequencies (down to 200 kHz). A signal-to-noise ratio of more than 100 to 1 has been achieved at about 1.36 MHz for crystalline B 2 0 3 . Two large n B responses have been found in vitreous B 2 0 3 (NMR detected only one site) with linewidths of less than 30 kHz. 27 A1 NQR spectra were obtained for OC-A1203 (Corundum), the mineral andalusite (a form of A1203 • Si0 2), and a glass having the composition of anorthite (CaO • A1203 • 2Si0 2).

Modeling the Influence of Acoustic Coupling of Hearing Aids on FM Signal-to-Noise Ratio

2015 ◽  
Vol 24 (2) ◽  
pp. 178-187
Author(s):  
Linda W. Norrix ◽  
Dianne Van Tasell ◽  
Jessie Ross ◽  
Frances P. Harris ◽  
James Dean
Keyword(s):  
Hearing Aids ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Signal To Noise ◽  
Low Frequencies ◽  
Acoustic Coupling ◽  
Primary Difference ◽  
Noise Ratio ◽  
Gain Loss ◽  
Open Versus

Purpose A model was developed to examine variables that influence signal-to-noise ratio (SNR) at the tympanic membrane (TM) when using a hearing aid (HA) and frequency modulated (FM) system. The model was used to explore how HA coupling influences SNR. Method To generate the model, HA output was measured in a coupler. Known coupler to real-ear transformations and known values for vent (gain) loss as a function of coupling were also used. The model was verified by measuring sound pressure level (SPL) at the TM in 6 ears. Results The model predicts similar overall SNRs at the TM regardless of coupling method when HA and FM microphones are active. The primary difference in SNR is in the low frequencies and depends on the amount of low frequency insertion gain and the noise levels at the HA and FM microphones. Conclusions A model was developed to explore how complex variables contribute to SNR at the TM. One variable, HA coupling, is predicted to have only a minimal effect on SNR at the TM when there is HA gain. Further studies will be needed to assess the real-world effectiveness of an FM system coupled to an open- versus closed-fit HA.

Identification of Flame Transfer Functions From LES of a Premixed Swirl Burner

2010 ◽  
Author(s):  
Luis Tay Wo Chong ◽  
Thomas Komarek ◽  
Roland Kaess ◽  
Stephan Fo¨ller ◽  
Wolfgang Polifke
Keyword(s):  
Transfer Function ◽  
Heat Release ◽  
Transfer Functions ◽  
Signal To Noise Ratio ◽  
Thermal Power ◽  
Reacting Flow ◽  
Signal To Noise ◽  
Swirl Burner ◽  
Low Frequencies ◽  
Noise Ratio

Large eddy simulations of compressible, turbulent, reacting flow were carried out in order to identify the Flame Transfer Function (FTF) of a premixed swirl burner at different power ratings. The Thickened Flame model with one step kinetics was used to model combustion. Time-averaged simulation results for inert and reacting flow cases were compared with experimental data for velocity and heat release distribution with good agreement. Heat losses at the combustor walls were found to have a strong influence on computed flame shapes and spatial distributions of heat release. For identification of the FTF with correlation analysis, broadband excitation was imposed at the inlet. At low power rating (30 kW), measured and computed FTFs agree very well at low frequencies (corresponding to Strouhal numbers St < 4), showing a pronounced maximum of the gain at St ≈ 2. At higher frequencies, where the flame response weakens, the agreement between experiment and computation deteriorates, presumably due to decreasing signal-to-noise ratio. At higher thermal power (50 kW), a high-frequency instability developed during the simulation runs, resulting in poor overall signal-to-noise ratio and thus to unsatisfactory prediction of the gain of the flame transfer function. The phase of the FTF, on the other hand, was predicted with good accuracy up to St < 5. An analytical expression for the FTF, which models the flame dynamics as a superposition of time-delayed responses to perturbations of mass flow rate and swirl number, respectively, was found to match the experimental results.

Sign in / Sign up

Export Citation Format

Share Document