Most of spectral analysis tools in SIGVIEW are based on the FFT algorithm. There are many
different parameters which can be applied to the signal before the FFT analysis is performed, to
the FFT calculation itself or its result.
If you want to change the default settings for spectral analysis calculation in SIGVIEW, use Signal
tools/Spectral analysis defaults option from the main menu. These settings will be applied to all
new FFT-based calculations, including FFT, Time-FFT, cross spectral analysis, etc.
Once the FFT or Time FFT is already calculated according to your current spectral analysis
defaults, you can edit those anytime by choosing Edit/Properties menu option for calculated
window (FFT, Time FFT,…). The same option is also available in the window’s
These settings will apply only to that single window.
The following parameters can be changed for each FFT based spectral analysis operation in SIGVIEW:
Subtract mean check box: Select it if you want to normalize the signal before processing. It
simply subtracts the mean value of the signal from each sample
Remove linear trend check box: If linear trend appears in signal ( i.e. the whole signal raises or
falls monotonously), it can affect evaluation of low frequency components in the FFT. This option
removes linear trend by subtracting linear least squares approximation from the signal
Remove values > : This option automatically removes values from the signal that are not in
range ( mean -N*StDev , mean + N*StDev ) where StDev is a standard deviation of the signal
and N is a user-defined coefficient entered in the corresponding edit box. Values are removed by
replacing them with the mean value.
Apply window: To avoid some undesirable effects of discrete Fourier transform, it is
recommended to lower the signal values near the end of the signal by multiplying it with some
appropriate weighting function. This technique is called “windowing”. SIGVIEW supports several
standard weight functions: Hann, Haming, Blackman, Triangle, Tukey. Choosing “Rectangle”
window has the same effect as turning windowing option off.
Zero padding: Due to the nature of the FFT algorithm, the fastest calculation will be performed
for signals having power-of-2 length (for example 128, 256, 512,…). If your signal or the part you
would like to analyze have some other length, there are several options you can choose:
· Never use zero padding: FFT or slower DFT algorithm will be applied on your signal
without altering or extending it with zeros. This is the slowest calculation method, but
the advantage is that your signal is not changed in any way. For prime number signal
lengths the calculation will be performed by using very slow DFT algorithm.
· Optimal method: Your signal will be expanded with zeros to the next possible length
allowing the usage of the FFT algorithm instead of very slow DFT.
· Next power of 2: Your signal will be expanded with zeros until the next power-of-2
length. This will enable the usage of fastest FFT algorithm.
To increase the precision of the spectral analysis without taking longer signal segments, you can
also expand existing signal segment with zeros, even beyond the next power-of-2 length. That
will not add essentially new information in the FFT result, but will increase its precision, i.e. will
reduce the size of the one frequency bin. You will simply get FFT result with more points - from
the same signal segment. (If signal has 1024 samples and you choose expanding by factor 8,
resulting spectrum will have 4096 pt. instead of 512 without using option). The result will be
comparable to interpolation of the normal FFT result. To use this feature, you can choose 2x, 4x
or 8x zero padding option.
· Instantaneous spectrum (no averaging): Each change in the signal will cause the
FFT to recalculate. This is the default behavior.
· Average last X spectrum results: This option is useful only for the FFT of the live
input signal or other fast changing signals. Last X complex FFTs will be averaged to
calculate the final result. That will decrease the influence of the noise in the signal
and is usually recommended for all spectral analysis functions on the live signal.
Show result as: After applying the FFT algorithm to the signal, the result will be an array of
complex numbers. These radio-buttons determine the way these complex numbers will be
converted to real values and displayed as FFT graphics.
· Magnitude: value generally regarded as “spectrum”; calculated as sqrt(R^2 + I^2)
· Power spectrum: Magnitude squared.
· Power spectral density (PSD): Power spectrum is calculated first and its values are divided
with the width of the frequency bin. This “normalization” makes comparisons between FFT
sequences from different signals with different sampling rates possible.
· Phase: show signal phase angle (in degrees)
· Real & Imaginary part: show only real or only imaginary part of the spectrum
Apply custom filter curve: You can apply custom filter curve to change the values of the
spectrum. See Custom filter curves section for
more information. This option is applicable only to
magnitude and power spectrum result types.
Logarithmic Y-Axis (dB units) option can be applied only to Magnitude, Power spectrum and
PSD spectrum results. It shows a logarithmic values for all FFT result points, causing Y-axis to be
Y-axis in dBFS: This option can be applied only if Logarithmic Y-Axis is used. It will calculate dB
values relative to some maximal, i.e. full scale value. You can freely define this Full-scale value in
signal units (for example 32767 for 16-bit sound card or max. voltage for NiDAQ device). There is
also a combo-box with the choice of some predefined values for the maximal amplitude. If you
set the full scale value to 0, dB values will always be calculated relative to the currently highest
FFT value so that the highest value is always equal to 0 dB and all other FFT values are
X-Axis units combo box: allows you to switch between following X-axis units: “Hz (Cycles/sec)”,
“CPM (Cycles/minute)”, “KHz”, “MHz”.
Smoothing: If the result of FFT is noisy and has many values, it can be useful to smooth it by
using weighted moving average function. That will remove small noisy details from the FFT and
give you a better overview of the important spectrum events. You can choose some of standard
weighting functions here and determine the length of a smoothing window (longer window means
Test for confidence: Siegel’s test for confidence of spectral peaks tells you if some peak in
spectrum is statistically significant or it could be product of some random fluctuation of the signal.
If you turn this option on, only significant peaks will be shown, while all other values in the
spectrum will be set to zero. You can choose two levels of confidence for this test: 95% or 99%.
This test is purely statistics; it does not use any artificial intelligence methods.