Background Noise Characteristics at the IberArray Broadband Seismic Network

2010 ◽  
Vol 100 (2) ◽  
pp. 618-628 ◽  
Author(s):  
J. Diaz ◽  
A. Villasenor ◽  
J. Morales ◽  
A. Pazos ◽  
D. Cordoba ◽  
...  
Keyword(s):  
Background Noise ◽  
Seismic Network ◽  
Noise Characteristics

High-frequency analysis of seismic background noise as a function of wind speed and shallow depth

1996 ◽  
Vol 86 (5) ◽  
pp. 1507-1515 ◽  
Author(s):  
Mitchell M. Withers ◽  
Richard C. Aster ◽  
Christopher J. Young ◽  
Eric P. Chael
Keyword(s):  
Wind Speed ◽  
High Frequency ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Shallow Depth ◽  
High Frequency Signal ◽  
Wind Speeds ◽  
Noise Characteristics ◽  
Cultural Noise ◽  
Seismic Background

Abstract We used a deep (1500 m) cased borehole near the town of Datil in west-central New Mexico to study high-frequency (>1 Hz) seismic noise characteristics. The remote site had very low levels of cultural noise, but strong winds (winter and spring) made the site an excellent candidate to study the effects of wind noise on seismograms. Along with a three-component set of surface sensors (Teledyne Geotech GS-13), a vertical borehole seismometer (GS-28) was deployed at a variety of depths (5, 43, and 85 m) to investigate signal and noise variations. Wind speed was measured with an anemometer. Event-triggered and time-triggered data streams were recorded on a RefTek 72-02 data acquisition system located at the site. Our data show little cultural noise and a strong correlation between wind speed and seismic background noise. The minimum wind speed at which the seismic background noise appears to be influenced varies with depth: 3 m/sec at the surface, 3.5 m/sec at 43 m in depth, and 4 m/sec at 85 m in depth. For wind speed below 3 to 4 m/sec, we observe omni-directional background noise that is coherent at frequencies below 15 Hz. This coherence is destroyed when wind speeds exceed 3 to 4 m/sec. We use a test event (Md ∼ 1.6) and superimposed noise to investigate signal-to-noise ratio (SNR) improvement with sensor depth. For the low Q valley fill of the Datil borehole (DBH) site, we have found that SNR can be improved by as much as 20 to 40 dB between 23 and 55 Hz and 10 to 20 dB between 10 and 20 Hz, by deploying at a 43-m depth rather than at the surface. At the surface, there is little signal above noise in the 23- to 55-Hz frequency band for wind speeds greater than 8 m/sec. Thus, high-frequency signal information that is lost at the surface can be recorded by deploying at the relatively shallow depth of 40 m. Because we observe only minor further reductions in seismic background noise (SBN) at deeper depths, 40 m is likely to be a reasonable deployment depth for other high-frequency-monitoring sites in similar environmental and geologic conditions.

A review on speech enhancement algorithms and why to combine with environment classification

2014 ◽  
Vol 25 (10) ◽  
pp. 1430002 ◽  
Author(s):  
S. Selva Nidhyananthan ◽  
R. Shantha Selva Kumari ◽  
A. Arun Prakash
Keyword(s):  
Real Time ◽  
Speech Enhancement ◽  
Background Noise ◽  
Estimation Algorithm ◽  
Noise Power ◽  
Noisy Speech ◽  
Noise Power Spectral Density ◽  
Noise Characteristics ◽  
Power Spectral ◽  
Mobile Phone Applications

Speech enhancement has been an intensive research for several decades to enhance the noisy speech that is corrupted by additive noise, multiplicative noise or convolutional noise. Even after decades of research it is still the most challenging problem, because most papers rely on estimating the noise during the nonspeech activity assuming that the background noise is uncorrelated (statistically independent of speech signal), nonstationary and slowly varying, so that the noise characteristics estimated in the absence of speech can be used subsequently in the presence of speech, whereas in a real time environment such assumptions do not hold for all the time. In this paper, we discuss the historical development of approaches that starts from the year 1970 to, the recent, 2013 for enhancing the noisy speech corrupted by additive background noise. Seeing the history, there are algorithms that enhance the noisy speech very well as long as a specific application is concerned such as the In-car noisy environments. It has to be observed that a speech enhancement algorithm performs well with a good estimation of the noise Power Spectral Density (PSD) from the noisy speech. Our idea pops up based on this observation, for online speech enhancement (i.e. in a real time environment) such as mobile phone applications, instead of estimating the noise from the noisy speech alone, the system should be able to monitor an environment continuously and classify it. Based on the current environment of the user, the system should adapt the algorithm (i.e. enhancement or estimation algorithm) for the current environment to enhance the noisy speech.

Research of Defect Detection Method Noise for Bevel Gear

2014 ◽  
Vol 889-890 ◽  
pp. 722-725 ◽  
Author(s):  
Feng Yan Dai ◽  
Zhao Yao Shi ◽  
Jia Chun Lin
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Background Noise ◽  
Back Propagation ◽  
Noise Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Bevel Gear ◽  
Noise Characteristics ◽  
Useful Signal ◽  
Gear Fault

Noise signal analysis method is widely available for gearbox bevel gear fault detection. However, the noise from the gearbox is usually concealed by background noise, which leads to poor efficiency analysis. This paper reports an ensemble empirical mode decomposition (EEMD) and neural network method for bevel gear fault detection. To extract useful signal, EEMD algorithm was firstly applied to get rid of the background noise. Characteristics from a group of discriminating defect status were then chosen to build the eigenvector. Finally, the eigenvector was imported into a back propagation (BP) neural network classifier for defect diagnosis automatically. Experimental results show that the proposed approach is capable for signal denoising and providing distinguishing characteristics of founded fault. The developed method is an accurate approach to detect fault for tested bevel gear.

Seismic Noise Characteristics at the Romanian Broadband Seismic Network

2012 ◽  
Vol 16 (5) ◽  
pp. 644-661 ◽  
Author(s):  
Bogdan Grecu ◽  
Cristian Neagoe ◽  
Dragos Tataru
Keyword(s):  
Seismic Noise ◽  
Seismic Network ◽  
Noise Characteristics

scholarly journals First results from VIRGO on SoHO

1997 ◽  
Vol 181 ◽  
pp. 67-82
Author(s):  
C. Fröhlich ◽  
B.N. Andersen ◽  
T. Appourchaux ◽  
G. Berthomieu ◽  
D.A. Crommelynck ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Background Noise ◽  
Active Regions ◽  
Magnetic Activity ◽  
Spatial Inhomogeneity ◽  
Resonant Frequencies ◽  
Heliospheric Observatory ◽  
Noise Characteristics ◽  
Nasa Mission ◽  
First Results

First results from the 4-6 months observations of the VIRGO experiment (Variability of solar IRradiance and Gravity Oscillations) on the ESA/NASA Mission SOHO (Solar and Heliospheric Observatory) are reported. The time series are evaluated in terms of solar irradiance variability, solar background noise characteristics and p-mode oscillations. The solar irradiance is modulated by the passage of active regions across the disk, but not all of the modulation is straightforwardly explained in terms of sunspot flux blocking and facular enhancement. The observed p-mode frequencies are more-or-less in agreement with earlier measurements, but it is interesting to note that systematic differences seem to exist between the observations in different colours. There is also evidence that magnetic activity plays a significant role in the dynamics of the oscillations beyond its modulation of the resonant frequencies. Moreover, by comparing the amplitudes of different components of p-mode multiplets, each of which are influenced differently by spatial inhomogeneity, we have found that activity enhances excitation.

scholarly journals Exploring the Relationship Between Working Memory, Compressor Speed, and Background Noise Characteristics

2016 ◽  
Vol 37 (2) ◽  
pp. 137-143 ◽  
Author(s):  
Barbara Ohlenforst ◽  
Pamela E. Souza ◽  
Ewen N. MacDonald
Keyword(s):  
Working Memory ◽  
Background Noise ◽  
Noise Characteristics ◽  
The Relationship
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