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):
The triangular smooth is like the rectangular smooth, above, except that it implements a weighted smoothing function. For a 5-point smooth (m = 5):
|Original unsmoothed noise||1|
|Smoothed white noise||0.1|
|Smoothed pink noise||0.55|
|Smoothed blue noise||0.01
Smoothed red (random walk) noise
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.
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.
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
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
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
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?
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 emphasize long-term behavior over short-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 determined visually or graphically or 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 the unsmoothed data, 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 the unsmoothed data; 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 to locate peaks but it should not be used to measure 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
common situation where you should not
smooth signals is prior to statistical procedures such
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, because smoothed 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.
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,
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 plot(x,mean(y)); the result shows a reduction in white noise by about sqrt(10)=3.2. This is enough to judge that there is a single peak with Gaussian shape, which can then be measured by curve fitting (covered in a later section) using the Matlab/Octave code peakfit([x;mean(y)],0,0,1), with the result showing excellent agreement with the position (500), height (2), and width (150) of the Gaussian peak created in the third line of the generating script (above left). A huge advantage of ensemble averaging is that the noise at all frequencies is reduced, not just the high-frequency noise as in smoothing.
Condensing 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.
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.
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.
Pittam has published a modification of the fastsmooth
function that tolerates NaNs (Not a Number) in the data file
and a version for smoothing angle data (nanfastsmoothAngle(Y,w,type,tol)).
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).
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
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.
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.