White noise

Four thousandths of a second of white noise

White noise () is a random signal (or process) with a flat power spectral density. In other words, the signal's power spectral density has equal power in any band, at any centre frequency, having a given bandwidth.

An infinite-bandwidth white noise signal is purely a theoretical construct. By having power at all frequencies, the total power of such a signal is infinite. In practice, a signal can be "white" with a flat spectrum over a defined frequency band.

Statistical properties

An example realization of a white noise process.

The term white noise is also commonly applied to a noise signal in the spatial domain which has zero autocorrelation with itself over the relevant space dimensions. The signal is then "white" in the spatial frequency domain (this is equally true for signals in the angular frequency domain, e.g. the distribution of a signal across all angles in the night sky). The image to the right displays a finite length, discrete time realization of a white noise process generated from a computer.

Being uncorrelated in time does not however restrict the values a signal can take. Any distribution of values is possible (although it must have zero DC component). For example, a binary signal which can only take on the values 1 or 0 will be white if the sequence of zeros and ones is statistically uncorrelated. Noise having a continuous distribution, such as a normal distribution, can of course be white.

It is often incorrectly assumed that Gaussian noise (see normal distribution) is necessarily white noise. However, neither property implies the other. Thus, the two words "Gaussian" and "white" are often both specified in mathematical models of systems. Gaussian white noise is a good approximation of many real-world situations and generates mathematically tractable models. These models are used so frequently that the term additive white Gaussian noise has a standard abbreviation: AWGN. Gaussian white noise has the useful statistical property that its values are independent (see Statistical independence).

White noise is the generalized mean-square derivative of the Wiener process or Brownian motion.

Colors of noise

There are also other "colors" of noise, the most commonly used being pink and brown.

Applications

One use for white noise is in the field of architectural acoustics. Here in order to submerge distracting, undesirable noises (for example conversations, etc.,) in interior spaces, a constant low level of noise is generated and provided as a background sound. White noise is used by some sirens for emergency vehicles, due to its ability to cut through background noise (e.g. urban traffic noise).

White noise has also been used in electronic music, where it is used either directly or as an input for a filter to create other types of noise signal. It is used extensively in audio synthesis, typically to recreate percussive instruments such as cymbals which have high noise content in their frequency domain.

It is also used to generate impulse responses. To set up the EQ for a concert or other performance in a venue, a short burst of pink noise is sent through the PA system and monitored from various points in the venue so that the engineer can tell if the acoustics of the building naturally boost or cut any frequencies. He or she can then adjust the overall EQ to ensure a balanced mix.

White noise is used as the basis of some random number generators.

White noise can be used to disorient individuals prior to interrogation and may be used as part of sensory deprivation techniques. White noise machines are sold as privacy enhancers and sleep aids.

Mathematical definition

White random vector

A random vector is a white random vector if and only if its mean vector and autocorrelation matrix are the following:

I.e., it is a zero mean random vector, and its autocorrelation matrix is a multiple of the identity matrix. When the autocorrelation matrix is a multiple of the identity, we say that it has spherical correlation.

White random process (white noise)

A continuous time random process w(t) where is a white noise process if and only if its mean function and autocorrelation function satisfy the following:

I.e., it is a zero mean process for all time and has infinite power at zero time shift since its autocorrelation function is the Dirac delta function.

The above autocorrelation function implies the following power spectral density.

since the Fourier transform of the delta function is equal to 1. Since this power spectral density is the same at all frequencies, we call it white as an analogy to the frequency spectrum of white light.

Random vector transformations

Two theoretical applications using a white random vector are the simulation and whitening of another arbitrary random vector. To simulate an arbitrary random vector, we transform a white random vector with a carefully chosen matrix. We choose the transformation matrix so that the mean and covariance matrix of the transformed white random vector matches the mean and covariance matrix of the arbitrary random vector that we are simulating. To whiten an arbitrary random vector, we transform it by a different carefully chosen matrix so that the output random vector is a white random vector.

These two ideas are crucial in applications such as channel estimation and channel equalization in communications and audio. These concepts are also used in data compression.

Simulating a random vector

Suppose that a random vector has covariance matrix Kxx. Since this matrix is Hermitian symmetric and positive semidefinite, by the spectral theorem from linear algebra, we can diagonalize or factor the matrix in the following way.

where E is the orthogonal matrix of eigenvectors and Λ is the diagonal matrix of eigenvalues.

We can simulate the 1st and 2nd moment properties of this random vector with mean and covariance matrix Kxx via the following transformation of a white vector :

where

Thus, the output of this transformation has expectation

and covariance matrix

Whitening a random vector

The method for whitening a vector with mean and covariance matrix Kxx is to perform the following calculation:

Thus, the output of this transformation has expectation

and covariance matrix

By diagonalizing Kxx, we get the following:

Thus, with the above transformation, we can whiten the random vector to have zero mean and the identity covariance matrix.

Random signal transformations

We can extend the same two concepts of simulating and whitening to the case of continuous time random signals or processes. For simulating, we create a filter into which we feed a white noise signal. We choose the filter so that the output signal simulates the 1st and 2nd moments of any arbitrary random process. For whitening, we feed any arbitrary random signal into a specially chosen filter so that the output of the filter is a white noise signal.

Simulating a continuous-time random signal

White noise fed into a linear, time-invariant filter to simulate the 1st and 2nd moments of an arbitrary random process.

We can simulate any wide-sense stationary, continuous-time random process with constant mean μ and covariance function

and power spectral density

We can simulate this signal using frequency domain techniques.

Because Kx(τ) is Hermitian symmetric and positive semi-definite, it follows that Sx(ω) is real and can be factored as

if and only if Sx(ω) satisfies the Paley-Wiener criterion.

If Sx(ω) is a rational function, we can then factor it into pole-zero form as

Choosing a minimum phase H(ω) so that its poles and zeros lie inside the left half s-plane, we can then simulate x(t) with H(ω) as the transfer function of the filter.

We can simulate x(t) by constructing the following linear, time-invariant filter

where w(t) is a continuous-time, white-noise signal with the following 1st and 2nd moment properties:

Thus, the resultant signal has the same 2nd moment properties as the desired signal x(t).

Whitening a continuous-time random signal

An arbitrary random process x(t) fed into a linear, time-invariant filter that whitens x(t) to create white noise at the output.

Suppose we have a wide-sense stationary, continuous-time random process defined with the same mean μ, covariance function Kx(τ), and power spectral density Sx(ω) as above.

We can whiten this signal using frequency domain techniques. We factor the power spectral density Sx(ω) as described above.

Choosing the minimum phase H(ω) so that its poles and zeros lie inside the left half s-plane, we can then whiten x(t) with the following inverse filter

We choose the minimum phase filter so that the resulting inverse filter is stable. Additionally, we must be sure that H(ω) is strictly positive for all so that Hinv(ω) does not have any singularities.

The final form of the whitening procedure is as follows:

so that w(t) is a white noise random process with zero mean and constant, unit power spectral density

Note that this power spectral density corresponds to a delta function for the covariance function of w(t).


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Note that this power spectral density corresponds to a delta function for the covariance function of w(t). One possibility could be current GMA Weekend weatherwoman Marysol Castro. so that w(t) is a white noise random process with zero mean and constant, unit power spectral density. It has not yet been announced who the new weatherman (or woman) will be. The final form of the whitening procedure is as follows:. Perkins affectionately said to his young child on the air, "Connor, if you're watching, daddy's comin' home.". Additionally, we must be sure that H(ω) is strictly positive for all so that Hinv(ω) does not have any singularities. Perkins announced that he was going to go home to his family and would be living in Washington, D.C., where he would go back to WTTG-TV, where he was previously a weather personality.

We choose the minimum phase filter so that the resulting inverse filter is stable. The last ten minutes of the day's show was dedicated to Perkins, where he gave thanks to one of the show's producers and a heartfelt goodbye to the three anchors, Charles Gibson, Diane Sawyer, and Robin Roberts. Choosing the minimum phase H(ω) so that its poles and zeros lie inside the left half s-plane, we can then whiten x(t) with the following inverse filter. On December 2, 2005, weatherman Tony Perkins left Good Morning America, where he has been the weather personality since 1999. We factor the power spectral density Sx(ω) as described above. Hartman signed off the show that day with his trademark close "From all of us, make it a good day." On that day Good Morning America became the first morning news show to broadcast in HDTV. We can whiten this signal using frequency domain techniques. Former co-hosts David Hartman and Joan Lunden, along with former meteorologist Spencer Christian were among the guests of honor.

Suppose we have a wide-sense stationary, continuous-time random process defined with the same mean μ, covariance function Kx(τ), and power spectral density Sx(ω) as above. On November 3, 2005, GMA celebrated its 30th birthday with recaps to 1975 and by decorating Times Square. Thus, the resultant signal has the same 2nd moment properties as the desired signal x(t). Good Morning America has won in timeslots in large markets like New York, which may give an indication that people may begin to choose them over The Today Show. where w(t) is a continuous-time, white-noise signal with the following 1st and 2nd moment properties:. As of 2005, Good Morning America has still not prevailed over The Today Show, though it has had a few one-show victories on the day after Pope John Paul II's funeral, and then a Mariah Carey concert shortly after in 2005. We can simulate x(t) by constructing the following linear, time-invariant filter. She had been regularly filling in for Diane Sawyer and Charlie Gibson up until then.

Choosing a minimum phase H(ω) so that its poles and zeros lie inside the left half s-plane, we can then simulate x(t) with H(ω) as the transfer function of the filter. In May 2005, ABC announced former ESPN anchor Robin Roberts, the show's news anchor would be promoted to co-anchor. If Sx(ω) is a rational function, we can then factor it into pole-zero form as. When he left to anchor WBBM-TV in Chicago, Robin Roberts replaced Mora. if and only if Sx(ω) satisfies the Paley-Wiener criterion. Until March 18, 2002, the news was anchored by Antonio Mora. Because Kx(τ) is Hermitian symmetric and positive semi-definite, it follows that Sx(ω) is real and can be factored as. ABC stuck with the Gibson and Sawyer team where they remain today as anchors of Good Morning America.

We can simulate this signal using frequency domain techniques. However, Good Morning America ratings once again increased and battled The Today Show for viewership, though it has not yet proclaimed a victory in weekly viewership over The Today Show. and power spectral density. The team was meant to be temporary until ABC could find permanent replacements. We can simulate any wide-sense stationary, continuous-time random process with constant mean μ and covariance function. It negotiated Gibson's return, teaming him up with Diane Sawyer. For whitening, we feed any arbitrary random signal into a specially chosen filter so that the output of the filter is a white noise signal. In 1999, ABC became desperate to revive Good Morning America which viewers disfavored.

We choose the filter so that the output signal simulates the 1st and 2nd moments of any arbitrary random process. The Today Show ratings skyrocketed and remained at the top spot into the mid 2000s. For simulating, we create a filter into which we feed a white noise signal. News and weather were anchored by Ann Curry and Al Roker. We can extend the same two concepts of simulating and whitening to the case of continuous time random signals or processes. By this time, The Today Show was hosted by Matt Lauer and Katie Couric. Thus, with the above transformation, we can whiten the random vector to have zero mean and the identity covariance matrix. With McRee and Newman at the helms of Good Morning America, long time viewers switched to The Today Show.

By diagonalizing Kxx, we get the following:. The show was almost killed when Gibson, too, left the show to make way for Kevin Newman in 1998. and covariance matrix. Lunden decided to step down after 17 years on the show, and was replaced by Lisa McRee. Thus, the output of this transformation has expectation. But Good Morning America would stumble from its top spot in 1997. The method for whitening a vector with mean and covariance matrix Kxx is to perform the following calculation:. Lunden and Gibson were a hard couple to beat.

and covariance matrix. Good Morning America sailed into the 1990s with its overwhelming ratings success. Thus, the output of this transformation has expectation. In 1983, CBS Morning beat The Today Show and took the second place spot after Good Morning America. where. It was hosted by Charles Kuralt and Diane Sawyer. We can simulate the 1st and 2nd moment properties of this random vector with mean and covariance matrix Kxx via the following transformation of a white vector :. But CBS decided it wanted to get aggressive in the morning news talk show ratings battle, and it launched CBS Morning, using the same format used on Good Morning America and The Today Show.

where E is the orthogonal matrix of eigenvectors and Λ is the diagonal matrix of eigenvalues. In the 1970s and 1980s, the CBS television network, aired only hard news stories during the morning time slot shared by Good Morning America and The Today Show. Since this matrix is Hermitian symmetric and positive semidefinite, by the spectral theorem from linear algebra, we can diagonalize or factor the matrix in the following way. Gibson and Lunden prevailed over The Today Show. Suppose that a random vector has covariance matrix Kxx. They became the most popular news partnership on television in the late 1980s and early 1990s. These concepts are also used in data compression. Lunden was paired with Charles Gibson and ratings skyrocketed for Good Morning America.

These two ideas are crucial in applications such as channel estimation and channel equalization in communications and audio. The partnership ended in February of 1987 as Hartman retired. To whiten an arbitrary random vector, we transform it by a different carefully chosen matrix so that the output random vector is a white random vector. Hartman and Lunden led the show through several seasons of success. We choose the transformation matrix so that the mean and covariance matrix of the transformed white random vector matches the mean and covariance matrix of the arbitrary random vector that we are simulating. In 1980, Hill left Good Morning America and was replaced by Joan Lunden. To simulate an arbitrary random vector, we transform a white random vector with a carefully chosen matrix. For the first time, Good Morning America became the highest rated morning news program in the United States as The Today Show fell to second place.

Two theoretical applications using a white random vector are the simulation and whitening of another arbitrary random vector. Good Morning America continued to threaten The Today Show into the 80's, especially after the abrupt removal of Brokaw from his anchor desk in 1981. Since this power spectral density is the same at all frequencies, we call it white as an analogy to the frequency spectrum of white light. Within a year, The Today Show managed to beat back the Good Morning America ratings threat with Brokaw and new co-host Jane Pauley, featuring Gene Shalit. since the Fourier transform of the delta function is equal to 1. On August 29, 1976, Tom Brokaw began anchoring The Today Show while a search was made for a female co-host. The above autocorrelation function implies the following power spectral density. Good Morning America ratings climbed slowly but steadily throughout the 1970s and into the 1980s while The Today Show experienced a slight slump in viewership, especially with Walters' decision to leave NBC for a job at ABC.

I.e., it is a zero mean process for all time and has infinite power at zero time shift since its autocorrelation function is the Dirac delta function. Dussault was replaced in 1977 by Sandy Hill. A continuous time random process w(t) where is a white noise process if and only if its mean function and autocorrelation function satisfy the following:. Good Morning America's first host was David Hartman, featuring Nancy Dussault as his co-host. When the autocorrelation matrix is a multiple of the identity, we say that it has spherical correlation. America in November 1975 as Good Morning America, taking its title from the chorus of the Steve Goodman song "City of New Orleans". I.e., it is a zero mean random vector, and its autocorrelation matrix is a multiple of the identity matrix. After rave reviews for the pilot, the format replaced A.M.

A random vector is a white random vector if and only if its mean vector and autocorrelation matrix are the following:. ABC took an episode of The Morning Exchange and used it as a pilot episode. White noise machines are sold as privacy enhancers and sleep aids. The result of all of this was ratings of nearly 70% for The Morning Exchange. White noise can be used to disorient individuals prior to interrogation and may be used as part of sensory deprivation techniques. Baker, felt the living room set would make viewers feel more comfortable. White noise is used as the basis of some random number generators. Perris and William F.

He or she can then adjust the overall EQ to ensure a balanced mix. The show's creators, Donald L. To set up the EQ for a concert or other performance in a venue, a short burst of pink noise is sent through the PA system and monitored from various points in the venue so that the engineer can tell if the acoustics of the building naturally boost or cut any frequencies. Also unlike both the NBC and ABC shows, The Morning Exchange was not broadcast from a newsroom set but instead one that resembled a suburban living room. It is also used to generate impulse responses. The Morning Exchange also established a group of regular guests who were experts in certain fields such as health, entertainment, consumer affairs, travel, etc. It is used extensively in audio synthesis, typically to recreate percussive instruments such as cymbals which have high noise content in their frequency domain. America and The Today Show, The Morning Exchange took less of a straightfoward news approach by offering news and weather updates only at the top and bottom of every hour and used the rest of the time discussing general-interest/entertainment topics.

White noise has also been used in electronic music, where it is used either directly or as an input for a filter to create other types of noise signal. Unlike A.M. urban traffic noise). America but instead was airing a locally produced show called The Morning Exchange. White noise is used by some sirens for emergency vehicles, due to its ability to cut through background noise (e.g. While looking around, they found that one of their affiliates, WEWS in Cleveland, was not broadcasting A.M. Here in order to submerge distracting, undesirable noises (for example conversations, etc.,) in interior spaces, a constant low level of noise is generated and provided as a background sound. The show could not find an audience against The Today Show, so ABC started to look for a new approach.

One use for white noise is in the field of architectural acoustics. ABC's show was hosted by Bill Beutel and Stephanie Edwards, with Peter Jennings reading the news. There are also other "colors" of noise, the most commonly used being pink and brown. America in an attempt to compete with the National Broadcasting Company (NBC) network production of The Today Show hosted by Jim Hartz and Barbara Walters. White noise is the generalized mean-square derivative of the Wiener process or Brownian motion. In January 1975, ABC launched A.M. Gaussian white noise has the useful statistical property that its values are independent (see Statistical independence). .

These models are used so frequently that the term additive white Gaussian noise has a standard abbreviation: AWGN. Since 2004, ABC has also aired Good Morning America Weekend Edition. Gaussian white noise is a good approximation of many real-world situations and generates mathematically tractable models. When major events happen in Washington during the morning hours, then the show is broadcast from Washington. Thus, the two words "Gaussian" and "white" are often both specified in mathematical models of systems. The program is currently hosted by Charles Gibson, Diane Sawyer, and Robin Roberts. However, neither property implies the other. It is the only network morning news program to broadcast in HDTV.

It is often incorrectly assumed that Gaussian noise (see normal distribution) is necessarily white noise. It is recorded live from Times Square Studios in New York City and fed to all network affiliates. Noise having a continuous distribution, such as a normal distribution, can of course be white. The show features news, weather, and special interest stories. For example, a binary signal which can only take on the values 1 or 0 will be white if the sequence of zeros and ones is statistically uncorrelated. The show was launched in 1975. Any distribution of values is possible (although it must have zero DC component). Good Morning America or GMA is the weekday morning news talk show of the American Broadcasting Company television network (ABC).

Being uncorrelated in time does not however restrict the values a signal can take. Bob Woodruff (as of 2004). The image to the right displays a finite length, discrete time realization of a white noise process generated from a computer. George Stephanopoulos (1997-2002). the distribution of a signal across all angles in the night sky). Wolfgang Puck (as of 2004). The signal is then "white" in the spatial frequency domain (this is equally true for signals in the angular frequency domain, e.g. Joel Siegel (as of 2004).

The term white noise is also commonly applied to a noise signal in the spatial domain which has zero autocorrelation with itself over the relevant space dimensions. Claire Shipman (as of 2004). . Nance (as of 2004). In practice, a signal can be "white" with a flat spectrum over a defined frequency band. John J. By having power at all frequencies, the total power of such a signal is infinite. Ann Pleshette Murphy (as of 2004).

An infinite-bandwidth white noise signal is purely a theoretical construct. David Muir (as of 2004). In other words, the signal's power spectral density has equal power in any band, at any centre frequency, having a given bandwidth. Sara Moulton (as of 2004). White noise () is a random signal (or process) with a flat power spectral density. Emeril Lagasse (as of 2004). Timothy Johnson (as of 2004).

Rebecca Kolls (as of 2004). Gregory Hunter (as of 2004). Mellody Hobson (as of 2004). Ron Hazelton (as of 2004).

Don Dahler (as of 2004). Bill Weir (as of 2004). Kate Snow (as of 2004). Robin Roberts (as of 2004).

Tony Perkins (1999-2005). Diane Sawyer (as of 2004). Charles Gibson (as of 2004).