scholarly journals The robustness of the differential quantizer in the case of the variable signal to noise ratio

2017 ◽  
Vol 14 (1) ◽  
pp. 149-160
Author(s):  
Lazar Cokic ◽  
Aleksandra Marjanovic ◽  
Sanja Vujnovic ◽  
Zeljko Djurovic
Keyword(s):  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Fixed Number ◽  
Signal To Noise ◽  
Input And Output ◽  
Noise Ratio ◽  
The Difference ◽  
Optimal Signal ◽  
Theoretical Overview

In this paper a short theoretical overview of differential quantizer and its implementations is given. Afterward, the effect of the order of prediction in differential quantizer and the effect of the difference in order of predictor in the input and output of differential quantizer is analyzed. Then it was proceeded with the examination of the robustness of the differential quantizer in the case in which a noise signal is brought to the input of the differential quantizer, instead of the clean speech signal. The analysis was conducted with a uniform distribution, as well as the noise with the gaussian distribution, and the obtained results were adequately commented on. Also, experimentally a limit was set which refers to the intensity of the noise and still enable results which are better that a regular uniform quantizer. The whole analysis is done by using the fixed number of bits in quantization, i.e. 12-bit quantizer is used in all the implementations of differential quantizer. In the conclusion of this paper there is a discussion about the possibility of implementing a differential quantizer which will be able to recognize which noise attacks the system, and in addition to that, in what form it adapts its coefficients so that it at any moment acquires the optimal signal to noise ratio.

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

scholarly journals Geometric Approach to Analytic Marginalisation of the Likelihood Ratio for Continuous Gravitational Wave Searches

Universe ◽  
2021 ◽  
Vol 7 (6) ◽  
pp. 174
Author(s):  
Karl Wette
Keyword(s):  
Gravitational Wave ◽  
Likelihood Ratio ◽  
Signal To Noise Ratio ◽  
Geometric Approach ◽  
The Other ◽  
Efficient Computation ◽  
Likelihood Ratios ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Optimal Signal

The likelihood ratio for a continuous gravitational wave signal is viewed geometrically as a function of the orientation of two vectors; one representing the optimal signal-to-noise ratio, and the other representing the maximised likelihood ratio or F-statistic. Analytic marginalisation over the angle between the vectors yields a marginalised likelihood ratio, which is a function of the F-statistic. Further analytic marginalisation over the optimal signal-to-noise ratio is explored using different choices of prior. Monte-Carlo simulations show that the marginalised likelihood ratios had identical detection power to the F-statistic. This approach demonstrates a route to viewing the F-statistic in a Bayesian context, while retaining the advantages of its efficient computation.

scholarly journals Optimal signal-to-noise ratio for silicon nanowire biochemical sensors

2011 ◽  
Vol 98 (26) ◽  
pp. 264107 ◽  
Author(s):  
Nitin K. Rajan ◽  
David A. Routenberg ◽  
Mark A. Reed
Keyword(s):  
Signal To Noise Ratio ◽  
Silicon Nanowire ◽  
Signal To Noise ◽  
Biochemical Sensors ◽  
Noise Ratio ◽  
Optimal Signal

Speech in noise testing before and after grommet insertion

2012 ◽  
Vol 126 (10) ◽  
pp. 1010-1015 ◽  
Author(s):  
V Possamai ◽  
G Kirk ◽  
A Scott ◽  
D Skinner
Keyword(s):  
Small Group ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Sound Field ◽  
Noise Signal ◽  
Signal To Noise ◽  
Speech In Noise ◽  
Before And After ◽  
Noise Test ◽  
Noise Ratio

AbstractObjectives:To assess the feasibility of designing and implementing a speech in noise test in children before and after grommet insertion, and to analyse the results of such a test in a small group of children.Methods:Twelve children aged six to nine years who were scheduled to undergo grommet insertion were identified. They underwent speech in noise testing before and after grommet insertion. This testing used Arthur Boothroyd word lists read at 60 dB in four listening conditions presented in a sound field: firstly in quiet conditions, then in signal to noise ratios of +10 (50 dB background noise), 0 (60 dB) and −10 (70 dB).Results:Mean phoneme scores were: in quiet conditions, 28.1 pre- and 30 post-operatively (p = 0.04); in 50 dB background noise (signal to noise ratio +10), 24.2 pre- and 29 post-operatively (p < 0.01); in 60 dB background noise (signal to noise ratio 0), 22.6 pre- and 27.5 post-operatively (p = 0.06); and in 70 dB background noise (signal to noise ratio −10), 13.9 pre- and 21 post-operatively (p = 0.05).Conclusion:This small study suggests that speech in noise testing is feasible in this scenario. Our small group of children demonstrated a significant improvement in speech in noise scores following grommet insertion. This is likely to translate into a significant advantage in the educational environment.

Mass Sensitivity or Gravimetric Satellites

2020 ◽  
Author(s):  
Robert Spero
Keyword(s):  
Signal To Noise Ratio ◽  
Satellite Tracking ◽  
Point Mass ◽  
Laser Ranging ◽  
Signal To Noise ◽  
Orbital Parameters ◽  
The Earth ◽  
Noise Ratio ◽  
Optimal Signal ◽  
Instrument Sensitivity

&lt;p class=&quot;p1&quot;&gt;A point mass on the surface of the Earth gives the highest frequency content for orbiting gravimetry, with&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160; &lt;/span&gt;the maximum frequency for gradiometers or satellite-to-satellite tracking determined by orbital altitude.&amp;#160; Frequency-domain expressions are found for&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160; &lt;/span&gt;measurements of a point-like source on the surface of the Earth.&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160; &lt;/span&gt;The response of orbiting gradiometers such as GOCE and satellite-to-satellite tracking missions such as GRACE-FO are compared. The optimal signal-to-noise ratio as a function&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160; &lt;/span&gt;of noise in the measurement apparatus is computed, and from that the minimum detectable mass is inferred. The point mass magnitude that gives signal-to-noise ratio = 3 is for GOCE&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160; &lt;/span&gt;M_3=200 Gton and&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160; &lt;/span&gt;for the laser ranging interferometer measurement on GRACE-FO&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160; &lt;/span&gt;M_3= 0.5 Gton. For the laser ranging interferometer measurement, the optimal filter for detecting point-like masses has a passband of 1 to 20 mHz,&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160; &lt;/span&gt;differing from the 0.3 to 20 mHz admittance filter of Ghobadi-Far et al. (2018), which is not specialized for detecting point-like masses. M_3 for&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160; &lt;/span&gt;future GRACE-like missions with different orbital parameters and improved instrument sensitivity is explored, and the optimum spacecraft separation is found.&lt;/p&gt;

scholarly journals Novel Configurations of Ultrahigh Frequency (≤600 MHz) Analog Frontend for High Resolution Ultrasound Measurement

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2598
Author(s):  
Min Kim ◽  
Jinhyoung Park ◽  
Qifa Zhou ◽  
Koping Shung
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Ultrahigh Frequency ◽  
Ultrasound Measurement ◽  
Functional Responses ◽  
Signal To Noise ◽  
Coded Excitation ◽  
Noise Ratio ◽  
The Difference

In this article, an approach to designing and developing an ultrahigh frequency (≤600 MHz) ultrasound analog frontend with Golay coded excitation sequence for high resolution imaging applications is presented. For the purpose of visualizing specific structures or measuring functional responses of micron-sized biological samples, a higher frequency ultrasound is needed to obtain a decent spatial resolution while it lowers the signal-to-noise ratio, the difference in decibels between the signal level and the background noise level, due to the higher attenuation coefficient. In order to enhance the signal-to-noise ratio, conventional approach was to increase the transmit voltage level. However, it may cause damaging the extremely thin piezoelectric material in the ultrahigh frequency range. In this paper, we present a novel design of ultrahigh frequency (≤600 MHz) frontend system capable of performing pseudo Golay coded excitation by configuring four independently operating pulse generators in parallel and the consecutive delayed transmission from each channel. Compared with the conventional monocycle pulse approach, the signal-to-noise ratio of the proposed approach was improved by 7–9 dB without compromising the spatial resolution. The measured axial and lateral resolutions of wire targets were 16.4 µm and 10.6 µm by using 156 MHz 4 bit pseudo Golay coded excitation, respectively and 4.5 µm and 7.7 µm by using 312 MHz 4 bit pseudo Golay coded excitation, respectively.

Proposition of deposition and bias conditions for optimal signal-to-noise-ratio in resistor- and FET-type gas sensors

Nanoscale ◽  
2020 ◽  
Vol 12 (38) ◽  
pp. 19768-19775 ◽  
Author(s):  
Wonjun Shin ◽  
Gyuweon Jung ◽  
Seongbin Hong ◽  
Yujeong Jeong ◽  
Jinwoo Park ◽  
...  
Keyword(s):  
Gas Sensors ◽  
Signal To Noise Ratio ◽  
Optimal Performance ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Optimal Signal

Response alone cannot fully evaluate the performance of sensors, and the signal-to-noise-ratio should additionally be considered to design gas sensors with optimal performance.

scholarly journals Effects of Using Static Methods with Contourlet Transformation on Speech Compression

2021 ◽  
pp. 2784-2795
Author(s):  
Esraa Abd Alsalam ◽  
Shaymaa Ahmed Razoqi ◽  
Eman Fathi Ahmed
Keyword(s):  
Speech Signal ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Speech Compression ◽  
Signal To Noise ◽  
Absolute Deviation ◽  
Limited Bandwidth ◽  
Normalized Cross Correlation ◽  
Noise Ratio ◽  
And Storage

Compression of speech signal is an essential field in signal processing. Speech compression is very important in today’s world, due to the limited bandwidth transmission and storage capacity. This paper explores a Contourlet transformation based methodology for the compression of the speech signal. In this methodology, the speech signal is analysed using Contourlet transformation coefficients with statistic methods as threshold values, such as Interquartile Filter (IQR), Average Absolute Deviation (AAD), Median Absolute Deviation (MAD) and standard deviation (STD), followed by the application of (Run length encoding) They are exploited for recording speech in different times (5, 30, and 120 seconds). A comparative study of performance of different transforms is made in terms of (Signal to Noise Ratio,Peak Signal to Noise Ratio,Normalized Cross-Correlation, Normalized Cross-Correlation) and the compression ratio (CR). The best stable result of implementing our algorithm for compressing speech is at level1 with   AAD or MAD, adopting Matlab 2013a language.

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