To generate a single number, use the 2-argument form of np.random.normal: In [47]: np.random.normal(0, 0.5) Out[47]: 0.6138972867165546 You may need to scale this number (multiply it by a small number epsilon) so the noise is small compared to self.x....

Clipping in general is what happens when a value exceeds some threshold and is forced to that threshold. Also known as saturation (when it's accidental), clamping (when deliberate) and others. It's common in digital systems, including digital photography, where you use a binary value with a specified number of bits...

python,numpy,random,noise,poisson

This is implicit in the algorithm to calculate the random sample of the poisson distribution. See the source code here. The random sample is calculated in a conditional loop, which gets a new random value and returns when this value is above some threshold based on lambda. For different lambda's...

Try this: **** The previous part of your code goes here *** disp('Recording complete'); [y,fs] = audioread('myspeech.wav'); y = y(:,1); % Take only one channel from the recording n = 7; beginFreq = 500 / (fs/2); endFreq = 2000 / (fs/2); [b,a] = butter(n, [beginFreq, endFreq], 'bandpass'); % dt =...

c#,2d,game-engine,noise,tiling

The input values to Perlin.GetValue(x, y, z) are doubles and not technically limited to the range 0.0-1.0 but I would recommend you take all your array indices and divide them with the length of the array in that dimension so they all fall in the range 0.0-1.0 and you should...

matlab,filter,signal-processing,noise

For your first question: Why did i need to pass the noise signal to a low pass filter in the first place? Supposing that the noise you have in your signal is high frequency (higher than the useful signal itself) this is the job of a low-pass filter. From Wikipedia:...

The Random class is designed to be a low overhead source of pseudo-random numbers. But the consequence of the "low overhead" implementation is that the number stream has properties that are a long way off perfect ... from a statistical perspective. You have encountered one of the imperfections. Random is...

One solution is to filter the Gaussian noise and then modulate it to a specific frequency band. Fs = 1000; L = 500; t = (0 : L-1)/Fs; x = chirp(t,10,.5,100); NFFT = 2^nextpow2(L); Y = fft(x,NFFT)/L; f = Fs / 2 * linspace(0,1,NFFT/2+1); subplot(211) plot(f,2*abs(Y(1:NFFT/2+1))) title('Amplitude Spectrum of Noise-free...

matlab,matrix,frequency,noise,sin

Added following line on top of your code: t = 0:186.52/(373046-1):186.52 ; Above vector hold time instants where we want to calculate the value of signal. Length of signal in time is 186.52 and want 373046 samples during time. So separation between two samples is 186.52/(373046-1) seconds....

This is still a pretty slow rng, but approximately 10 times faster than the Murmur3 based. Reseeding for every generated number has a cost, so does requiring a large number of seeds that all have a non-systematic influence on the outcome. Update: There really isn't any reason to allow weak...

matlab,fft,noise,spectrum,mathcad

The main reason why it doesn't work is due to the scaling factor of the FFT between MathCad and MATLAB. With MathCad, there is an extra scaling factor of 1/sqrt(N) whereas MATLAB does not include this said scaling factor. As such, you'll need to multiply your FFT results by this...

matlab,signal-processing,noise

Your vectors don't have the same size. S is 1x2001 and W is 1x2500. Try W = M + sqrt(V)*rand(size(S)); Then you can just add the signals by SW = S + W; As Kostya already wrote, awgn can be used if you know the desired SNR....

I have had similar problems before and after alot of tweaking and testing I've come to the conclusion that just plain 2D perlin noise as is will never look like natural terrain, it's essentially white noise(ie no huge mountains or valleys, just hills close together) What I recently found as...

c++,boost,noise,normal-distribution

Here's my take on it: #include <boost/random/normal_distribution.hpp> #include <boost/random.hpp> int main() { boost::mt19937 gen(42); // seed it once boost::normal_distribution<double> nd(0.0, 1.0); boost::variate_generator<boost::mt19937&, boost::normal_distribution<double> > randNormal(gen, nd); std::vector<double> data(100000, 0.0), nsyData; nsyData.reserve(data.size()); double sd = 415*randNormal(); std::transform(data.begin(), data.end(), std::back_inserter(nsyData),...

python,ios,swift,accelerometer,noise

I've used some simple easing that smoothes out any spikes in the values. It'll add a bit of latency, but you can determine the balance of latency vs. smoothness to suit your application by adjusting the easing property. import UIKit import CoreMotion class MyViewController: UIViewController { var displayLink: CADisplayLink? let...

The algorithm mentioned is NOT mentioned for noise cleaning. The algorithm separates different continuous areas. You have decided to use the algorithm for to find and separate the main blot. Why not. But it seems, you have found several close small blots, too. As it is, it seems that your...

The are many parts than can make noise in a computer. If the sound only appears under heavy load, and it isn't some kind of metallic sound, probably it will come from voltage regulators or pwm fans. See more here: http://electronics.stackexchange.com/a/34813...

Turns out my microphone sensitivity was too high. very, very high to be exact. It was at 100, meaning that it would pick up the smallest sounds(such as background noise). My guess is that those small sounds would be amplified to such a high degree that the SpeechRecognitionEngine would have...