The color burst is also an indicator that there is a color signal.
This is for compatibility with black and white signals. No color burst means B&W signal, so only decode the luminance signal (no croma).
No signal, no color burst, so the decoder falls back to B&W mode.
Same idea goes to FM stereo/mono. If there is no 19 kHz subcarrier present, ...
In the absence of a valid color burst signal, the "color killer" circuit disables the color difference signals, otherwise you would indeed see colored noise. This is mainly intended for displaying weak signals in B/W without the colored noise.
One step further is to mute the entire signal, substitute stable sync signals, and display a blue or black field ...
Isn't white noise supposed to have a flat magnitude response?
(equal amounts for all frequencies)
The expected magnitude response of white noise is flat (this is what JasonR calls the power spectral density). Any particular instance of a white noise sequence will not have precisely flat response (this is what JasonR's comment refers to as the power ...
White Gaussian noise in the continuous-time case is not what is called a second-order process (meaning $E[X^2(t)]$ is finite) and so, yes, the variance is infinite. Fortunately, we can never observe a white noise process (whether
Gaussian or not) in nature; it is only observable through some kind of device,
e.g. a (BIBO-stable) linear filter with transfer ...
The first approach in Peter's answer (i.e. filtering white noise) is a very straightforward approach. In Spectral Audio Signal Processing, JOS gives a low-order filter that can be used to produce a decent approximation, along with an analysis of how well the resulting power spectral density matches the ideal. Linear filtering will always ...
You would generate bandlimited Gaussian noise by first generating white noise, then filtering it to the bandwidth that you desire. As an example:
% design FIR filter to filter noise to half of Nyquist rate
b = fir1(64, 0.5);
% generate Gaussian (normally-distributed) white noise
n = randn(1e4, 1);
% apply to filter to yield bandlimited noise
nb = filter(b,1,...
You can use a standard inpainting algorithm. These algorithms replace marked pixels in an image with the pixel values that surround these marked pixels. The challenge here is to detect the grid (my tests seem to show that it is not a completely regular grid). So, I came up with this solution:
from PIL import Image
from io import BytesIO
Yes, you can add AWGN of variance $\sigma^2$ separately to each of the two terms, because the sum of two Gaussians is also a Gaussian and their variances add up. This will have the same effect as adding an AWGN of variance $2\sigma^2$ to the original signal. Here's some more explanation if you're interested.
An analytic signal $x(t)=a(t)\sin\left(2\pi f t + ...
L1 norm minimization (compressed sensing) can do a relative better job than conventional Fourier denoising in terms of preserving edges.
The procedure is to minimize an objective function
|x-y|^2 + b|f(y)|
where $x$ is the noisy signal, $y$ is the denoised signal, $b$ is the regularziation parameter, and $|f(y)|$ is some L1 norm penalty. ...
One method that works if there's a relatively strong drum beat is to take the magnitude of the STFT of the waveform, and then auto-correlate it in only the time dimension. The peak of the auto-correlation function will be the beat, or a submultiple of it.
This is equivalent to breaking up the signal into a lot of different frequency bands, finding the ...
Intuition: The intuition is this: Your noise is some event or events that are rare, and that when compared to other events, look like outliers that shouldn't really be there.
For example, if you are measuring the speeds of every car on the highway as they pass by you and plot them, you will see that they are usually in the range of say, $50$ mph to $70$ ...
Noise is random, but like most random phenomena, it follows a certain pattern. Different patterns are given different names.
Consider rolling a die. This is clearly random. Roll the die 1000 times, keeping track of each result. Then, calculate the histogram of the result; you'll find that you got each of 1, 2, 3, 4, 5 and 6 approximately the same number of ...
You could form a statistical test, based on the autocorrelation of the potentially-white sequence. The Digital Signal Processing Handbook suggests the following.
This may be implemented in scilab as below.
Running this function over two noise sequences: a white noise one, and a lightly filtered white noise one, then the following plot results. Script for ...
Your question is a bit harsh, because it's kind of vague. I will give you a few points, maybe it will help.
What's the same?
The intuitions behind both bilateral filtering and anisotropic diffusion are the same:
averaging is good to remove random noise;
averaging should only concern pixels that belong to the same region (in the sense that they are pixels ...
Roughly speaking, they are the amount of noise in your system. Process noise is the noise in the process - if the system is a moving car on the interstate on cruise control, there will be slight variations in the speed due to bumps, hills, winds, and so on. Q tells how much variance and covariance there is. The diagonal of Q contains the variance of each ...
Basic dithering without noise shaping
Basic dithered quantization without noise shaping works like this:
Figure 1. Basic dithered quantization system diagram. Noise is zero-mean triangular dither with a maximum absolute value of 1. Rounding is to nearest integer. Residual error is the difference between output and input, and is calculated for analysis only....
There is another Wikipedia entry on Wiener filtering more applicable to image processing.
To summarize (and convert to 2D), given a system:
y(n,m) = h(n,m) * x(n,m) + v(n,m)
$*$ denotes convolution,
$x$ is the (unknown) true image,
$h$ is the impulse response of a linear, time-invariant filter,
$v$ is additive unknown noise independent of $x$...
I read your original question and wasn't quite sure what you were getting at but it's quite a lot clearer now. The problem you have is that the brain is extremely good at picking out speech and emotion even when the background noise is very high which is your existing attempts have only been of limited success.
I think the key to getting what you want is ...
Starting at an even more basic level than the other (much smarter) answers, I'd like to pick up on this part of the question:
This seems contradictory to me as on one side it is random then on the other side their distribution is considered normally distributed.
Perhaps the issue here is what ‘random’ means?
To be clear: ‘random’ and ‘normally-...
You might need to consider more advanced techniques. Here are two recent papers on edge-preserving denoising:
Edge-Preserving Image Denoising via Optimal Color Space Projection [in color] This paper preserves edges by decomposing the image into an "optimal" color space and performing wavelet shrinkage. The optimal color space belongs to the luminance/color-...
According to papers below, snoring is characterized by a peak at about 130Hz, and is wholly concentrated below 12kHz:
Non-invasive Sensors based Human State in Nightlong Sleep Analysis for Home-Care
An efficient fast method of snore detection for sleep disorder investigation
An efficient method for snore/nonsnore classification of sleep sounds
You're probably looking for the Hough transform or one of it's extensions.
The simplest version of this transform is linear and appropriate for detecting straight lines.
In the transformed space (Hough space), angles and distances are found as points where curves intersect.
Libraries for calculating the Hough transform exist in
C++ - OpenCV (Has ...
We can do the calculation using some basic elements of probability theory and Fourier analysis.
There are three elements (we denote the probability density of a random variable $X$ at value $x$ as $P_X(x)$):
Given a random variable $X$ with distribution $P_X(x)$, the distribution of the scaled variable $Y = aX$ is $P_Y(y) = (1/a)P_X(y/a)$.
I'm not sure specifically what you're looking for here. Noise is typically described via its power spectral density, or equivalently its autocorrelation function; the autocorrelation function of a random process and its PSD are a Fourier transform pair. White noise, for example, has an impulsive autocorrelation; this transforms to a flat power spectrum in ...
Just as a for instance, de-clicking might be considered a part of a de-noising system. Removing clicks comes up in digitizing vinyl audio records - dust that cannot be removed without damaging the substrate can cause an audible click in the digitized audio signal. There are systems that can detect and remove these clicks that use model based estimators to ...
De-noising is about the goal, and filtering is about the technique you employ.
You can obviously de-noise via filtering. For example, if you know that your system cannot transmit frequencies above a certain threshold, you can apply a low-pass filter. However, you can de-noise by other techniques as well, such as by averaging multiple recordings of a signal....
Just throwing this in here to cover all the possibilities, you might be able to use entropy, I don't known what the entropy level of snoring vs speech is but if it is different enough that may work.
"Noise" in this context refers to anything unwanted added to the signal, it doesn't necessarily mean it is gaussian noise, white noise, or any random well-described process.
In the context of quantization, it is a purely algebraic argument. One can view quantization as the addition of an unwanted signal ("noise") equal to... the difference between the ...
Wiener deconvolution is an approach to solve the deconvolution problem that relies on the filter proposed by Wiener. The equation is the same in denoising and deblurring, except that the filter $G$ (to stick with Wikipedia's notations) that you should use is different.
To make things clear:
denoising consists in the case where the degradation kernel $H$ is ...