[Introduction]
[Signal
arithmetic] [Signals
and noise] [Smoothing]
[Differentiation]
[Peak
Sharpening] [Harmonic
analysis] [Fourier
convolution] [Fourier
deconvolution] [Fourier
filter] [Peak
area measurement] [Linear
Least Squares] [Multicomponent
Spectroscopy] [Iterative
Curve Fitting] [Hyperlinear
quantitative absorption spectrophotometry] [Appendix
and Case Studies] [Peak
Finding and Measurement] [iPeak]
[iSignal]
[Peak
Fitters] [iFilter]
[iPower]
[List
of downloadable software] [Interactive
tools]

[Smoothing Algorithms] [Noise Reduction] [End Effects] [Examples] [The problem with smoothing] [Optimization] [When should you smooth a signal?] [When should you NOT smooth a signal?] [Dealing with spikes] [Video Demonstration] [Spreadsheets] [Matlab/Octave] [Interactive tools] [Have a question? Email me]

In many experiments in science, the true signal
amplitudes (y-axis values) change rather smoothly as a function of
the x-axis values, whereas many kinds of noise are seen as rapid,
random changes in amplitude from point to point within the signal. In
the latter situation it may be useful in some cases to attempt to
reduce the noise by a process called *smoothing*.
In smoothing, the data points of a signal are modified so that
individual points that are *higher *than
the immediately adjacent points (presumably because of noise) are
reduced, and points that are *lower *than
the adjacent points are increased. This naturally leads to a smoother
signal (and a slower step response to signal changes) . As long as
the true underlying signal is actually smooth, then the true signal
will not be much distorted by smoothing, but the high frequency noise
will be reduced. In terms of the frequency
components of a signal, a smoothing operation acts as a low-pass
filter, reducing the high-frequency components and passing the
low-frequency components with little change.

**Smoothing algorithms**.
Most
smoothing algorithms are based on the "*shift
and multiply*" technique, in which a
group of adjacent points in the original data are multiplied
point-by-point by a set of numbers (coefficients) that defines the
smooth shape, the products are added up and divided by the sum of the
coefficients, which becomes one point of smoothed data, then the set
of coefficients is shifted one point down the original data and the
process is repeated. The simplest
smoothing algorithm is the *rectangular
boxcar *or *unweighted
sliding-average smooth*; it simply replaces
each point in the signal with the average of *m*
adjacent points, where *m*
is a positive integer called the *smooth width*.
For example, for a 3-point smooth (*m*
= 3):

for j = 2 to n-1, where S_{j}
the j^{th} point in
the smoothed signal, Y_{j}
the j^{th} point in
the original signal, and n is the total number of points in the
signal. Similar smooth operations can be constructed for any desired
smooth width, *m*.
Usually *m* is an odd
number. If the noise in the data is "white noise" (that is,
evenly distributed over all frequencies) and its standard deviation
is *D*, then the
standard deviation of the noise remaining in the signal after the
first pass of an unweighted sliding-average smooth will be
approximately s over the square root of *m*
(*D*/sqrt(*m*)),
where *m* is the smooth
width. Despite its simplicity, this
smooth is actually optimum for the common problem of reducing
white noise while keeping the *sharpest step
response*. The response to a step change is in
fact *linear*, so this
filter has the advantage of responding completely with no residual
effect withing its *response time*,
which is equal to the smooth width divided by the sampling rate.

The *triangular smooth *is like the rectangular
smooth, above, except that it implements a *weighted *smoothing
function. For a 5-point smooth (*m* = 5):

for
j = 3 to n-2, and similarly for other smooth widths (see the
spreadsheet UnitGainSmooths.xls).
In both of these cases, the integer in the denominator is the *sum
of the coefficients* in the numerator, which
results in a “unit-gain” smooth that has no effect on the
signal where it is a straight line and which preserves the area
under peaks.

It is often useful to apply a smoothing
operation more than once, that is, to smooth an already smoothed
signal, in order to build longer and more complicated smooths. For
example, the 5-point triangular smooth above is equivalent to two
passes of a 3-point rectangular smooth. *Three
*passes of a 3-point rectangular smooth result
in a 7-point "*pseudo-Gaussian*"
or *haystack *smooth,
for which the coefficients are in the ratio 1:3:6:7:6:3:1. The
general rule is that *n*
passes of a *w*-width
smooth results in a combined smooth width of *n***w*-*n*+1.
For example, 3 passes of a 17-point smooth results in a 49-point
smooth. These multi-pass smooths are more effective at reducing
high-frequency noise in the signal than a rectangular smooth but
exhibit slower step response.

In all these smooths, the width
of the smooth *m* is
chosen to be an odd integer, so that the smooth coefficients are
symmetrically balanced around the central point, which is important
because it preserves the x-axis position of peaks and other features
in the signal. (This is especially critical for analytical and
spectroscopic applications because the peak positions are often
important measurement objectives).

Note that we are
assuming here that the x-axis intervals of the signal is uniform,
that is, that the difference between the x-axis values of adjacent
points is the same throughout the signal. This is also assumed
in many of the other signal-processing techniques described in this
essay, and it is a very common (but not necessary) characteristic of
signals that are acquired by automated and computerized equipment.

The *Savitzky-Golay*
smooth is based on the least-squares fitting of polynomials to
segments of the data. The algorithm is discussed in
http://www.wire.tu-bs.de/OLDWEB/mameyer/cmr/savgol.pdf.
Compared to the sliding-average smooths, the Savitzky-Golay smooth is
less effective at reducing noise, but more effective at retaining the
shape of the original signal. It is capable of differentiation
as well as smoothing. The algorithm is more complex and the
computational times are greater than the smooth types discussed
above, but with modern computers the difference is not significant
and code
in various languages is widely available online. See
SmoothingComparison.html.

The
shape of any smoothing algorithm can be determined by applying that
smooth to a *delta function*,
a signal consisting of all zeros except for one point, as
demonstrated by the simple Matlab/Octave script DeltaTest.m.**Noise
reduction**. Smoothing
usually reduces the noise in a signal. If the noise is "white"
(that is, evenly distributed over all frequencies) and its standard
deviation is *D*, then
the standard deviation of the noise remaining in the signal after one
pass of a rectangular smooth will be approximately *D*/sqrt(*m*),
where *m* is the smooth
width. If a triangular smooth is used instead, the noise will be
slightly less, about *D**0.8/sqrt(*m*).
Smoothing operations can be applied more than once: that is, a
previously-smoothed signal can be smoothed again. In some cases this
can be useful if there is a great deal of high-frequency noise in the
signal. However, the noise reduction for white noise is less in each
successive smooth. For example, *three *passes
of a rectangular smooth reduces white noise by a factor of
approximately *D**0.7/sqrt(*m*),
only a slight improvement over two passes.

The
frequency distribution of noise, designated by noise
color, substantially effects the ability of smoothing to
reduce noise. The Matlab/Octave function “NoiseColorTest.m”
compares the effect of a 20-point boxcar (unweighted sliding average)
smooth on the standard deviation of white, pink, and blue noise, all
of which have an original unsmoothed
standard deviation of 1.0. Because smoothing is a low-pass filter
process, it effects low frequency (pink and red) noise less, and
effects high-frequency (blue and violet) noise more, than it does
white noise.

Original unsmoothed noise |
1 |

Smoothed |
0.1 |

Smoothed |
0.55 |

Smoothed |
0.01 |

Smoothed |
0.98 |

Note that the computation
of standard deviation is independent of the order of the data and
thus of its frequency distribution; sorting a set of data does not
change its standard deviation. The standard deviation of a sine wave
is independent of its frequency. Smoothing, however, changes both the
frequency distribution and standard deviation of a data set.

**End effects and the lost
points problem.** In
the equations above, the 3-point rectangular smooth is defined only
for j = 2 to n-1. There is not enough data in the signal to define a
complete 3-point smooth for the first point in the signal (j = 1) or
for the last point (j = n) , because there are no data points before
the first point or after the last point. (Similarly, a 5-point smooth
is defined only for j = 3 to n-2, and therefore a smooth can not be
calculated for the first two points or for the last two points). In
general, for an *m*-width
smooth, there will be (*m*-1)/2
points at the beginning of the signal and (*m*-1)/2
points at the end of the signal for which a complete *m*-width
smooth can not be calculated the usual way. What to do? There are two
approaches. One is to accept the loss of points and trim off those
points or replace them with zeros in the smooth signal. (That's the
approach taken in most of the figures in this paper). The other
approach is to use *progressively smaller
smooths* at the ends of the signal, for
example to use 2, 3, 5, 7... point smooths for signal points 1, 2,
3,and 4..., and for points n, n-1, n-2, n-3..., respectively. The
later approach may be preferable if the edges of the signal contain
critical information, but it increases execution time. The fastsmooth
function discussed below can utilize either of
these two methods.

**Examples of smoothing**.
A simple example of smoothing is shown in Figure 4. The left half of
this signal is a noisy peak. The right half is the same peak after
undergoing a triangular smoothing algorithm. The noise is greatly
reduced while the peak itself is hardly changed. The reduced noise
allows the signal characteristics (peak position, height, width,
area, etc.) to be measured more accurately by visual inspection.

*Figure 4. The left half of this signal is a noisy
peak. The right half is the same peak after undergoing a ***smoothing***
algorithm. The noise is greatly reduced while the peak itself is
hardly changed, making it easier to measure the peak position,
height, and width directly by graphical or visual estimation (but it
does not improve measurements made by least-squares methods; see
below).*

The larger the smooth width, the greater the noise
reduction, but also the greater the possibility that the signal will
be *distorted* by the smoothing operation. The optimum choice of
smooth width depends upon the width and shape of the signal and the
digitization interval. For peak-type signals, the critical factor is
the *smooth ratio*, the ratio between the smooth width *m*
and the number of points in the half-width of the peak. In general,
increasing the smoothing ratio improves the signal-to-noise ratio but
causes a reduction in amplitude and in increase in the bandwidth of
the peak. Be aware that the smooth width can be expressed in two
different ways: (a) as the number of data points or (b) as the x-axis
interval (for spectroscopic data usually in nm or in frequency
units). The two are simply related: the number of data points is
simply the x-axis interval times the increment between adjacent
x-axis values. The *smooth ratio* is the same in either case.

The figures above show examples of the effect of
three different smooth widths on noisy Gaussian-shaped peaks. In the
figure on the left, the peak has a (true) height of 2.0 and there are
80 points in the half-width of the peak. The red line is the original
unsmoothed peak. The three superimposed green lines are the results
of smoothing this peak with a triangular smooth of width (from top to
bottom) 7, 25, and 51 points. Because the peak width is 80 points,
the *smooth ratios* of
these three smooths are 7/80 = 0.09, 25/80 = 0.31, and 51/80 = 0.64,
respectively. As the smooth width increases, the noise is
progressively reduced but the peak height also is reduced slightly.
For the largest smooth, the peak width is slightly increased. In the
figure on the right, the original peak (in red) has a true height of
1.0 and a half-width of 33 points. (It is also less noisy than the
example on the left.) The three superimposed green lines are the
results of the same three triangular smooths of width (from top to
bottom) 7, 25, and 51 points. But because the peak width in this case
is only 33 points, the *smooth ratios*
of these three smooths are larger - 0.21, 0.76, and 1.55,
respectively. You can see that the peak distortion effect (reduction
of peak height and increase in peak width) is greater for the
narrower peak because the smooth ratios are higher. Smooth ratios of
greater than 1.0 are seldom used because of excessive peak
distortion. Note that even in the worst case, the peak positions are
not effected (assuming that the original peaks were symmetrical and
not overlapped by other peaks). If retaining the shape of the peak is
more important than optimizing the signal-to-noise ratio, the
Savitzky-Golay has the advantage over sliding-average smooths. In all
cases, the total area under the peak
remains unchanged. If the peak widths vary substantially, an adaptive
smooth, which allows the smooth width to vary across the signal,
may be used.

**The
problem with smoothing **is
that *it is often
less beneficial than you might think*.
It's important to point out that smoothing results such as
illustrated in the figure above may be *deceptively impressive*
because they employ a *single sample* of a noisy signal that is
smoothed to different degrees. This causes the viewer to
underestimate the contribution of *low-frequency* noise, which
is hard to estimate visually because there are so few low-frequency
cycles in the signal record. This problem can visualized by recording
a number of independent samples of a noisy signal consisting of a
single peak, as illustrated in the two figures below. These figures
show ten superimposed plots with the same peak but with independent
white noise, each plotted with a different line color, unsmoothed on
the left and smoothed on the right. Inspection of the smoothed
signals on the right clearly shows the variation in peak position,
height, and width between the 10 samples caused by the low frequency
noise remaining in the smoothed signals. Without the noise, each peak
would have a peak height of 2, peak center at 500, and width of 150.
Just because a signal looks smooth does not mean there is no noise.
Low-frequency noise remaining in the signals after smoothing will
still interfere with precise measurement of peak position, height,
and width.

x=1:1000; |
x=1:1000; |

(The generating scripts below each figure require that the functions gaussian.m, whitenoise.m, and fastsmooth.m be downloaded from http://tinyurl.com/cey8rwh.)

It should be clear that smoothing can seldom
*completely *eliminate noise, because most noise is spread out
over a wide range of frequencies, and smoothing simply reduces the
noise in *part *of its frequency range. Only for some very
specific types of noise (e.g. discrete frequency noise or
single-point spikes) is there hope of anything close to complete
noise elimination. Smoothing *does* make the signal smoother and
*it does* reduce the standard deviation of the noise, but
whether or not that makes for a *better measurement* or not
depends on the situation. And don't assume that just because a little
smoothing is good that more will necessarily be better. Smoothing is
like alcohol; sometimes you really need it - but you should never
overdo it.

The figure on the right below is another example
signal that illustrates some of these principles. The signal consists
of two Gaussian peaks, one located at x=50 and the second at x=150.
Both peaks have a peak height of 1.0 and a peak half-width of 10, and
a normally-distributed random white noise with a standard deviation
of 0.1 has been added to the entire signal. The x-axis sampling
interval, however, is different for the two peaks; it's 0.1 for the
first peak (from x=0 to 100) and 1.0 for the second peak (from x=100
to 200). This means that the first peak is characterized
by ten times more points that the second peak. It may *look*
like the first peak is noisier than the second, but that's just an
illusion; the signal-to-noise ratio for both peaks is 10. The second
peak looks less noisy only because there are fewer noise samples
there and we tend to underestimate the dispersion of small samples.
The result of this is that when the signal is smoothed, the second
peak is much more likely to be distorted by the smooth (it becomes
shorter and wider) than the first peak. The first peak can tolerate a
much wider smooth width, resulting in a greater degree of noise
reduction. (Similarly, if both peaks are measured with the
least-squares curve fitting
method, the fit of the first peak
is more stable with the noise and the measured parameters of that
peak will be about *3 times more accurate *than
the second peak, because there are 10 times more data points in that
peak, and the measurement precision improves roughly with the square
root of the number of data points if the noise is white). You
can download the data file "udx" in TXT
format or in Matlab MAT format.

**Optimization of
smoothing.** As
smooth width increases, the smoothing ratio increases, noise is
reduced quickly at first, then more slowly, and the peak height is
also reduced, slowly at first,
then more quickly. The *noise reduction*
depends on the smooth width, the smooth type (e.g. rectangular,
triangular, etc), and the noise color, but the *peak
height reduction* also depends on the peak
width. The result is that the signal-to-noise (defined as the ratio
of the peak height of the standard deviation of the noise) increases
quickly at first, then reaches a maximum. This is illustrated in the
animation on the left for a Gaussian peak with white noise (produced
by this Matlab/Octave script). The
maximum improvement in the signal-to-noise ratio depends on the
number of points in the peak: the more points in the peak, the
greater smooth widths can be employed and the greater the noise
reduction. This figure also illustrates that most of the noise
reduction is due to *high frequency*
components of the noise, whereas much of the *low
*frequency noise remains in the signal even as
it is smoothed.

Which is the best smooth ratio? It depends on
the purpose of the peak measurement. If the ultimate objective of the
measurement is to measure the peak height or width, then smooth
ratios below 0.2 should be used and the *Savitzky-Golay*
smooth is preferred. But if the objective of the measurement is to
measure the peak position (x-axis value of the peak), larger smooth
ratios can be employed if desired, because smoothing has little
effect on the peak position (unless peak is asymmetrical or the
increase in peak width is so much that it causes adjacent peaks to
overlap). If the peak is actually formed of two underlying peaks
that overlap so much that they appear to be one peak, then curve
fitting is the only way to measure the parameters of the
underlying peaks. Unfortunately, the optimum signal-to-noise ratio
corresponds to a smooth ratio that significantly distorts the peak,
which is why curve fitting the unsmoothed data is often preferred.

In *quantitative chemical analysis* applications
based on calibration by standard samples, the peak height reduction
caused by smoothing is not so important. If the *same* signal
processing operations are applied to the samples and to the
standards, the peak height reduction of the standard signals will be
exactly the same as that of the sample signals and the effect will
cancel out exactly. In such cases smooth widths from 0.5 to 1.0 can
be used if necessary to further improve the signal-to-noise ratio, as
shown in the figure on the left (for a simple sliding-average
rectangular smooth). In practical analytical chemistry, absolute peak
height measurements are seldom required; calibration against standard
solutions is the rule. (Remember: the objective of quantitative
analysis is not to measure a signal but rather to measure the
concentration of the unknown.) It is very important, however, to
apply *exactly* the same signal processing steps to the standard
signals as to the sample signals, otherwise a large systematic error
may result.

For a more detailed comparison of all four smoothing types considered above, see SmoothingComparison.html.

**When should you smooth a
signal?** There
are two reasons to smooth a signal:

(a)for cosmetic reasons, to prepare a nicer-looking or more dramatic graphic of a signal for visual inspection or publications, especially in order to emphasizelong-termbehavior overshort-term, or(b)if the signal will be subsequently analyzed by a method that would be degraded by the presence of too much high-frequency noise in the signal, for example if the heights of peaks are to be determinedvisually or graphicallyor by using the MAX function, of the the widths of peaks is measured by the halfwidth function, or if the location of maxima, minima, or inflection points in the signal is to be determined automatically by detecting zero-crossings in derivatives of the signal. Optimization of the amount and type of smoothing is important in these cases (see Differentiation.html#Smoothing). But generally, if a computer is available to make quantitative measurements, it's better to use least-squares methods on theunsmootheddata, rather than graphical estimates on smoothed data. If a commercial instrument has the option to smooth the data for you, it's best to disable the smoothing and record and save theunsmootheddata; you can always smooth it yourself later for visual presentation and it will be better to use the unsmoothed data for an least-squares fitting or other processing that you may want to do later. Smoothing can be used tolocate peaksbut it should not be used tomeasure peaks.

Care must be used in the design of algorithms that
employ smoothing. For example, in a popular technique for peak
finding and measurement, peaks are located by detecting downward
zero-crossings in the smoothed first
derivative, but the position, height, and width of each peak is
determined by least-squares curve-fitting
of a segment of original *unsmoothed*
data in the vicinity of the zero-crossing. That way, even if
heavy smoothing is necessary to provide reliable discrimination
against noise peaks, the peak parameters extracted by curve fitting
are not distorted by the smoothing.

**When should you NOT
smooth a signal? **One
common situation where you
should * not*
smooth signals is prior to statistical procedures
such as least-squares curve
fitting, because:

(a)smoothing will not significantly improve the accuracy of parameter measurement by least-squares measurements between separate independent signal samples,(b)all smoothing algorithms are at least slightly "lossy", entailing at least some change in signal shape and amplitude,(c)it is harder to evaluate the fit by inspecting the residuals if the data are smoothed, becausesmoothed noise may be mistaken for an actual signal, and(d)smoothing the signal will seriously underestimate the parameters errors predicted by propagation-of-error calculations and the bootstrap method.

**Dealing with spikes and
outliers. **Sometimes
signals are contaminated with very tall, narrow “spikes”
or "outliers" occurring at random intervals and with random
amplitudes, but with widths of only one or a few points. It not only
looks ugly, but it also upsets the assumptions of least-squares
computations because it is not *normally-distributed*
random noise. This type of interference is difficult to eliminate
using the above smoothing methods without distorting the signal.
However, a “median” filter, which replaces each point in
the signal with the *median*
(rather than the average) of *m*
adjacent points, can completely eliminate narrow spikes with little
change in the signal, if the width of the spikes is only one or a few
points and equal to or less than *m*.
See http://en.wikipedia.org/wiki/Median_filter.
The killspikes.m function uses a different
approach; it locates and eliminates the spikes by "patches over
them" using linear interpolation from the signal before and
after. Unlike conventional smooths, these functions can be profitably
applied *prior *to
least-squares fitting functions. (On the other hand, if it's the
*spikes *that are
actually the signal of interest, and other components of the signal
are interfering with their measurement, see CaseStudies.html#G).
**An
alternative to smoothing **to
reduce noise in the set of ten unsmoothed signals
used above is ensemble
averaging, which can be performed in this case very simply by the
Matlab/Octave code

**Con****densing
oversampled signals**. Sometimes
signals are recorded more densely (that is, with smaller x-axis
intervals) than really necessary to capture all the important
features of the signal. This results in larger-than-necessary data
sizes, which slows down signal processing procedures and may tax
storage capacity. To correct this, oversampled signals can be
reduced in size either by eliminating data points (say, dropping
every other point or every third point) or by replacing groups of
adjacent points by their averages. The later approach has the
advantage of *using*
rather than *discarding*
extraneous data points, and it acts like smoothing to provide some
measure of noise reduction. (If the noise in the original signal is
white, and the signal is condensed by averaging every *n*
points, the noise is reduced in the condensed signal by the square
root of *n*, but with *no
change* in frequency distribution of the
noise). The Matlab/Octave script testcondense.m
demonstrates the effect of boxcar averaging using the condense.m
function to reduce noise without changing the noise color. Shows that
the boxcar reduces the measured noise,
removing the high frequency components
but has little effect on the the peak parameters. Least-squares curve
fitting on the condensed data is faster
and results in a lower fitting error, but *no
more accurate measurement* of peak parameters.

**Video Demonstration.**
This 18-second, 3 MByte video (Smooth3.wmv)
demonstrates the effect of triangular smoothing on a single Gaussian
peak with a peak height of 1.0 and peak width of 200. The initial
white noise amplitude is 0.3, giving an initial signal-to-noise ratio
of about 3.3. An attempt to measure the peak amplitude and peak width
of the noisy signal, shown at the bottom of the video, are initially
seriously inaccurate because of the noise. As the smooth width is
increased, however, the signal-to-noise ratio improves and the
accuracy of the measurements of peak amplitude and peak width are
improved. However, above a smooth width of about 40 (smooth ratio
0.2), the smoothing causes the peak to be shorter than 1.0 and wider
than 200, even though the signal-to-noise ratio continues to improve
as the smooth width is increased. (This demonstration was created in
Matlab 6.5.

SPECTRUM, the freeware Macintosh signal-processing application, includes rectangular and triangular smoothing functions for any number of points.

**Spreadsheets.**
Smoothing
can be done in spreadsheets using the "shift and multiply"
technique described above. In the
spreadsheets smoothing.ods and
smoothing.xls (screen
image) the set of multiplying coefficients is contained in the
formulas that calculate the values of each cell of the smoothed data
in columns C and E. Column C performs a 7-point rectangular smooth (1
1 1 1 1 1 1) and column E does a 7-point triangular smooth (1 2 3 4 3
2 1), applied to the data in column A. You can type in (or Copy and
Paste) any data you like into column A, and you can extend the
spreadsheet to longer columns of data by dragging the last row of
columns A, C, and E down as needed. But to change the smooth width,
you would have to change the equations in columns C or E and copy the
changes down the entire column. It's common practice to divide the
results by the sum of the coefficients so that the net gain is unity
and the area under the curve of the smoothed signal is preserved.
The spreadsheets UnitGainSmooths.xls
and UnitGainSmooths.ods
(screen image) contain a collection
of unit-gain convolution coefficients for rectangular, triangular,
and Gaussian smooths of width 3 to 29 in both vertical (column) and
horizontal (row) format. You can Copy and Paste these into your own
spreadsheets.

The spreadsheets MultipleSmoothing.xls
and MultipleSmoothing.ods (screen
image) demonstrate a more flexible method in which the
coefficients are contained in a group of 17 adjacent cells (in row 5,
columns I through Y), making it easier to change the smooth shape and
width (up to a *maximum *of
17) just by changing those 17 cells. (To make a smaller smooth, just
insert zeros for the unused coefficients; in this example, a 7-point
triangular smooth is defined in columns N - T and the rest of the
coefficients are zeros ). In this spreadsheet, the smooth is applied
*three times* in
succession, resulting in an effective maximum smooth width of
n*w-n+1 = 49 points applied to column G.

Compared to
Matlab/Octave, spreadsheets are much slower, less flexible, and less
easily automated. For example, in these spreadsheets, to change the
signal or the number of points in the signal, or to change the smooth
width or type, you have to modify the spreadsheet in several places,
whereas to do the same using the Matlab/Octave "fastsmooth"
function (below), you need only change the input arguments of a
single line of code. And combining several different techniques
into one spreadsheet is more complicated than writing a Matlab/Octave
script that does the same thing.

**Smoothing in Matlab**
and **Octave**.
The
custom function fastsmooth implements
shift and multiply type smooths
using
a recursive algorithm. (Click on this link to inspect the code,
or right-click to download for use within Matlab). "Fastsmooth"
is a Matlab function of the form **s=fastsmooth(a,w,
type, edge)**. The argument "a" is
the input signal vector; "w" is the smooth width (a
positive integer); "type" determines the smooth type:
type=1 gives a rectangular (sliding-average or boxcar) smooth; type=2
gives a triangular smooth, equivalent to two passes of a sliding
average; type=3 gives a pseudo-Gaussian smooth, equivalent to three
passes of a sliding average; these shapes are compared in the figure
on the left. (See SmoothingComparison.html
for a comparison of these smoothing modes). The argument "edge"
controls how the "edges" of the signal (the first w/2
points and the last w/2 points) are handled. If edge=0, the edges are
zero. (In this mode the elapsed time is independent of the smooth
width. This gives the fastest execution time). If edge=1, the edges
are smoothed with progressively smaller smooths the closer to the
end. (In this mode the execution time increases with increasing
smooth widths). The smoothed signal is returned as the vector "s".
(You can leave off the last two input arguments: fastsmooth(Y,w,type)
smooths with edge=0 and fastsmooth(Y,w) smooths with type=1 and
edge=0). Compared to convolution-based smooth algorithms, fastsmooth
uses a simple recursive algorithm that typically gives much faster
execution times, especially for large smooth widths; it can smooth a
1,000,000 point signal with a 1,000 point sliding average in less
than 0.1 second. Here's a simple example of fastsmooth demonstrating
the effect on white noise (graphic).`x=1:100;y=randn(size(x));
plot(x,y,x,fastsmooth(y,5,3,1),'r')xlabel('Blue: white
noise. Red: smoothed white noise.')`

SegmentedSmooth.m,
illustrated on the right, is a segmented multiple-width data
smoothing function, based on the fastsmooth algorithm, which can be
useful if the widths of the peaks or the noise level varies
substantially across the signal. *The syntax is
the same as* *fastsmooth.m*,
except that the second input argument "smoothwidths" can be
a *vector*: `SmoothY
= SegmentedSmooth (Y, smoothwidths, type, ends)`.
The function divides Y into a number of equal-length regions defined
by the length of the vector 'smoothwidths', then smooths each region
with a smooth of type 'type' and width defined by the elements of
vector 'smoothwidths'. In the graphic example in the figure on the
right, `smoothwidths=[31 52
91]`, which divides up the signal into three
regions and smooths the first region with smoothwidth 31, the second
with smoothwidth 51, and the last with smoothwidth 91. *Any
number of smooth widths and sequence of smooth widths can be used*.
Type "help SegmentedSmooth" for other examples examples.
DemoSegmentedSmooth.m
demonstrates the operation with different signals consisting of noisy
variable-width peaks that get progressively wider, like the figure on
the right. If the peak widths increase regularly across the signal,
you can calculate a reasonable initial value for the smoothwidths
vector by giving only the number of segments (NumSegments) , the
first value, startw and the last value, endw:

wstep=(endw-startw)/NumSegments;

smoothwidths=startw:wstep:endw;

Diederick has published a Savitzky-Golay smooth function in Matlab, which you can download from the Matlab File Exchange. It's included in the iSignal function. Greg Pittam has published a modification of the fastsmooth function that tolerates NaNs (Not a Number) in the data file (nanfastsmooth(Y,w,type,tol)) and a version for smoothing angle data (nanfastsmoothAngle(Y,w,type,tol)).

SmoothWidthTest.m
is a simple script that uses the fastsmooth function to demonstrate
the effect of smoothing on peak height, noise, and signal-to-noise
ratio of a peak. You can change the peak shape in line 7, the smooth
type in line 8, and the noise in line 9. A typical result for a
Gaussian peak with white noise smoothed with a pseudo-Gaussian smooth
is shown on the left. Here, as it is for most peak shapes, the
optimal signal-to-noise ratio occurs at a smooth ratio of about 0.8.
However, that optimum corresponds to a *significant
reduction in the peak height*, which could be
a serious problem. A smooth width about *half
*the width of the original unsmoothed peak
produces less distortion of the peak but still achieves a reasonable
noise reduction. SmoothVsCurvefit.m
is a similar script, but is also compares curve
fitting as an alternative method to measure the peak height
*without smoothing*.

This effect is explored more completely by the
text below, which shows an experiment in
Matlab or Octave that creates a Gaussian peak, smooths it,
compares the smoothed and unsmoothed version, then uses the max,
halfwidth, and trapz functions to print out
the *peak height, halfwidth, and area*.
(max and trapz are both built-in functions in Matlab and Octave, but
you have to download halfwidth.m. To learn
more about these functions, type "help" followed by the
function name).

x=[0:.1:10]';

y=exp(-(x-5).^2);

plot(x,y)

ysmoothed=fastsmooth(y,11,3,1);

plot(x,y,x,ysmoothed,'r')

disp([max(y)
halfwidth(x,y,5) trapz(x,y)])

disp([max(ysmoothed)
halfwidth(x,ysmoothed,5) trapz(x,ysmoothed)]

1 1.6662
1.7725

0.78442
2.1327 1.7725

These
results show that smoothing *reduces
*the
peak height (from 1 to 0.784) and *increases
*the
peak width (from 1.66 to 2.13), but has *no
effect*
on the peak area, as long as you measure the *total
area*
under the broadened peak.

Smoothing
is useful if the signal is contaminated by non-normal noise such as
sharp spikes or if the peak height, position, or width are measured
by simple methods, but there is no need to smooth the data if the
noise is white and the peak parameters are measured by least-squares
methods, because the results obtained on the unsmoothed data will be
more accurate (see CurveFittingC.html#Smoothing).

The Matlab/Octave user-defined function condense.m*,
condense(y,n)*, returns a condensed version of
*y* in which each group
of *n* points is
replaced by its average, reducing the length of *y*
by the factor *n*. (For
*x,y* data sets, use
this function on both independent variable *x*
**and** dependent variable
*y* so that the features
of *y* will appear at
the same *x* values).

The Matlab/Octave user-defined function
medianfilter.m, *medianfilter(y,w)*,
performs a median-based filter operation that replaces each value of
*y* with the median of *w*
adjacent points (which must be a positive integer). killspikes.m
is a threshold-based filter for eliminating narrow spike artifacts.
The syntax is fy= killspikes(x, y, threshold, width). Each
time it finds a positive or negative jump in the data between y(n)
and y(n+1) that exceeds "threshold", it replaces the next
"width" points of data with a linearly interpolated segment
spanning x(n) to x(n+width+1), See killspikesdemo. Type
"help killspikes" at the command prompt.

ProcessSignal
is a Matlab/Octave command-line function that performs smoothing and
differentiation on the time-series data set x,y (column or row
vectors). It can employ all the types of smoothing described
above. Type "help ProcessSignal". Returns the processed
signal as a vector that has the same shape as x, regardless of the
shape of y. The syntax is **Processed=ProcessSignal(x,
y, DerivativeMode, w, type, ends, Sharpen, factor1, factor2,
SlewRate, MedianWidth)**

**iSignal**
is
an interactive function for Matlab that performs smoothing for
time-series signals using *all the algorithms
discussed above*, including the Savitzky-Golay
smooth, a median filter, and a condense function, with keystrokes
that allow you to adjust the smoothing parameters continuously while
observing the effect on your signal instantly, making it easy to
observe how different types and amounts of smoothing effect noise and
signal, such as the height, width, and areas of peaks. (Other
functions include differentiation, peak sharpening, interpolation,
least-squares peak measurement, and a frequency spectrum mode that
shows how smoothing and other functions can change the frequency
spectrum of your signals). The simple script “iSignalDeltaTest”
demonstrates the frequency response of iSignal's smoothing functions
by applying them to a single-point
spike, allowing you to change the smooth type and the smooth
width to see how the the frequency response changes. View the code
here or download the ZIP
file with sample data for testing.

**iSignal** for Matlab. Click to view
larger figures.

**You try it:**
Here's an example of a very noisy signal with lots of high-frequency
(blue) noise *totally obscuring a perfectly
good peak* in the center at x=150,
height=1e-4; SNR=90. First, download NoisySignal
into the Matlab path, then execute these statements:

`>> load NoisySignal>>
isignal(x,y);`

Use the **A** and **Z** keys to increase
and decrease the smooth width, and the **S** key to cycle through
the available smooth types. Hint: use the Gaussian smooth and keep
increasing the smooth width until the peak shows.

**Note:** you can right-click on any of the
m-file links on this site and select **Save Link As...** to
download them to your computer for use within Matlab. Unfortunately,
iSignal does not currently work in Octave.

An
earlier version of his page is available in French, at
http://www.besteonderdelen.nl/blog/?p=4169,
courtesy of Natalie Harmann and Anna
Chekovsky.

Last updated February,
2017. This page is part of "**A
Pragmatic Introduction to Signal Processing**",
created and maintained by Prof.
Tom O'Haver , Department of Chemistry and Biochemistry, The
University of Maryland at College Park. Comments, suggestions, bug
reports, and questions should be directed to Prof. O'Haver at
toh@umd.edu.

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