Contents
How do you whiten a signal?
The whitening process is often used for ambient vibration data before stacking waveforms for cross-correlation. The process is simple as Fourier transforming the signal after applying Hann window, then normalizing its magnitude, and then inverse Fourier transforming it.
What is Zca?
ZCA Whitening is an image preprocessing method that leads to a transformation of data such that the covariance matrix is the identity matrix, leading to decorrelated features.
Why is decorrelation important?
In image processing decorrelation techniques can be used to enhance or stretch, color differences found in each pixel of an image. The concept of decorrelation can be applied in many other fields. In neuroscience, decorrelation is used in the analysis of the neural networks in the human visual system.
What is Velvet Noise decorrelation?
Decorrelation of audio signals is a critical step for spatial sound reproduction on multichannel configurations. This paper proposes a method to optimize the decorrelation properties of a sparse noise sequence, called velvet noise, to generate short FIR decorrelation filters.
How are signals represented in the time domain?
Time domain representation of an electrical signal. Signals can also be represented by a magnitude and phase as a function of frequency. Signals that repeat periodically in time are represented by a power spectrum as illustrated in Figure 2.
Why do we need a White Noise domain?
This domain is handy because all the frequencies move together in space unless we have dispersion. The latter we can deal with by operating in relatively small (say 21 traces by 300msec) windows overlapping in time and space. It is important to realize that white noise is random and hence unpredictable.
How is current expressed in the time domain?
In the time domain, voltage or current is expressed as a function of time as illustrated in Figure 1. Most people are relatively comfortable with time domain representations of signals. Signals measured on an oscilloscope are displayed in the time domain and digital information is often conveyed by a voltage as a function of time.
How to remove white noise from seismic data?
Two modules (4d,5dDEC) for removing white noise from seismic data will be presented. They employ principal component decomposition in the time domain. (Jones – 1985) Because all frequencies move together in a spatial sense, this domain offers a considerable advantage in processing the low amplitudes at the top end of the wavelet’s spectrum.