`WNoiseGenerator`#

class acoular.signals.WNoiseGenerator

Bases: NoiseGenerator

White noise signal generator.

This class generates white noise signals with a specified root mean square (RMS) amplitude, number of samples, and sampling frequency. The white noise is generated using a random number generator initialized with a user-defined seed for reproducibility.

See also

numpy.random.RandomState.standard_normal: Used here to generate normally distributed noise.
PNoiseGenerator: For pink noise generation.
UncorrelatedNoiseSource: For per-channel noise generation.

Examples

Generate white noise with an RMS amplitude of 1.0 and 0.5:

>>> from acoular import WNoiseGenerator
>>> from numpy import mean
>>>
>>> # White noise with RMS of 1.0
>>> gen1 = WNoiseGenerator(rms=1.0, num_samples=1000, seed=42)
>>> signal1 = gen1.signal()
>>>
>>> # White noise with RMS of 0.5
>>> gen2 = WNoiseGenerator(rms=0.5, num_samples=1000, seed=24)
>>> signal2 = gen2.signal()
>>>
>>> mean(signal1) > mean(signal2)
np.True_

Ensure different outputs with different seeds:

>>> gen1 = WNoiseGenerator(num_samples=3, seed=42)
>>> gen2 = WNoiseGenerator(num_samples=3, seed=73)
>>> gen1.signal() == gen2.signal()
array([False, False, False])

digest: A unique identifier for the generator, based on its properties. (read-only)

signal()

Generate and deliver the white noise signal.

The signal is created using a Gaussian distribution with mean 0 and variance 1, scaled by the RMS amplitude of the object.

Returns:

numpy.ndarray: A 1D array of floats containing the generated white noise signal. The length of the array is equal to num_samples.

usignal(factor)

Resample the signal at a higher sampling frequency.

This method uses Fourier transform-based resampling to deliver the signal at a sampling frequency that is a multiple of the original sample_freq. The resampled signal has a length of factor * num_samples.

Parameters:

factorint: The resampling factor. Defines how many times larger the new sampling frequency is compared to the original sample_freq.

Returns:

numpy.ndarray: The resampled signal as a 1D array of floats.

Notes

This method relies on the scipy.signal.resample() function for resampling.

Examples

Resample a signal by a factor of 4:

>>> from acoular import SineGenerator  # Class extending SignalGenerator
>>> sg = SineGenerator(sample_freq=100.0, num_samples=1000)
>>> resampled_signal = sg.usignal(4)
>>> len(resampled_signal)
4000

rms: Root mean square (RMS) amplitude of the signal. For a point source, this corresponds to the RMS amplitude at a distance of 1 meter. Default is 1.0.

seed: Seed for random number generator. Default is 0. This parameter should be set differently for different instances to guarantee statistically independent (non-correlated) outputs.

sample_freq: Sampling frequency of the signal in Hz. Default is 1.0.

num_samples: The number of samples to generate for the signal.

WNoiseGenerator#

`WNoiseGenerator`#