Signal Metrics

This section defines some useful functions of signals (vectors).

The *mean* of a
signal
(more precisely the ``sample mean'') is defined as the
*average value* of its samples:^{5.5}

The *total energy*
of a signal
is defined as the *sum of squared moduli*:

In physics, energy (the ``ability to do work'') and work are in units
of ``force times distance,'' ``mass times velocity squared,'' or other
equivalent combinations of units.^{5.6} In digital signal processing, physical units are routinely
discarded, and signals are renormalized whenever convenient.
Therefore,
is defined above without regard for constant
scale factors such as ``wave impedance'' or the sampling interval
.

The *average power* of a signal
is defined as the *energy
per sample*:

Another common description when is real is the

Power is always in physical units of energy per unit time. It therefore
makes sense to define the average signal power as the total signal energy
divided by its length. We normally work with signals which are functions
of time. However, if the signal happens instead to be a function of
distance (*e.g.*, samples of displacement along a vibrating string), then the
``power'' as defined here still has the interpretation of a *spatial
energy density*. Power, in contrast, is a *temporal energy density*.

The *root mean square* (RMS) level of a signal
is simply
. However, note that in practice (especially in audio
work) an RMS level is typically computed after subtracting out any
nonzero mean value.

The *variance* (more precisely the *sample variance*) of the
signal
is defined as the power of the signal with its mean
removed:^{5.7}

It is quick to show that, for real signals, we have

which is the ``mean square minus the mean squared.'' We think of the variance as the power of the non-constant signal components (

The *norm* (more specifically, the *
norm*, or
*Euclidean norm*) of a signal
is defined as the square root
of its total energy:

We think of as the

[How to cite this work] [Order a printed hardcopy] [Comment on this page via email]

Copyright ©

Center for Computer Research in Music and Acoustics (CCRMA), Stanford University