A Quadrature Signals Tutorial: Complex, But Not Complicated

Understanding the 'Phasing Method' of Single Sideband Demodulation

Complex Digital Signal Processing in Telecommunications

Introduction to Sound Processing

Introduction of C Programming for DSP Applications

**Search Spectral Audio Signal Processing**

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*White* noise may be defined as a sequence of *uncorrelated*
random values, where correlation is defined in Appendix C and
discussed further below. Perceptually, white noise is a wideband
``hiss'' in which all frequencies are equally likely. In Matlab or
Octave, band-limited white noise can be generated using the
`rand` or
`randn` functions:

y = randn(1,100); % 100 samples of Gaussian white noise % with zero mean and unit variance x = rand(1,100); % 100 white noise samples, % uniform between 0 and 1. xn = 2*(x-0.5); % Make it uniform between -1 and +1True white noise is obtained in the limit as the sampling rate goes to infinity and as time goes to plus and minus infinity. In other words, we never work with true white noise, but rather a finite time-segment from a white noise which has been band-limited to less than half the sampling rate and sampled.

In making white noise, it doesn't matter how the amplitude values are
distributed probabilistically. That is, the relative probability of
different amplitudes at any single sample instant does not affect
whiteness, provided there is *some* zero-mean distribution of
amplitude. It only matters that successive samples of the sequence
are *uncorrelated*. This means that knowing some of the samples
does not help at all in predicting other samples. Further discussion
regarding white noise appears in §C.3.

Julius Smith's background is in electrical engineering (BS Rice 1975, PhD Stanford 1983). He is presently Professor of Music and (by courtesy) of Electrical Engineering at Stanford's Center for Computer Research in Music and Acoustics (CCRMA), teaching courses and pursuing research related to signal processing applied to music and audio systems. See http://ccrma.stanford.edu/~jos/ for details.

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