scipy.signal.check_COLA¶

scipy.signal.
check_COLA
(window, nperseg, noverlap, tol=1e10)[source]¶ Check whether the Constant OverLap Add (COLA) constraint is met
Parameters: window : str or tuple or array_like
Desired window to use. If window is a string or tuple, it is passed to
get_window
to generate the window values, which are DFTeven by default. Seeget_window
for a list of windows and required parameters. If window is array_like it will be used directly as the window and its length must be nperseg.nperseg : int
Length of each segment.
noverlap : int
Number of points to overlap between segments.
tol : float, optional
The allowed variance of a bin’s weighted sum from the median bin sum.
Returns: verdict : bool
True if chosen combination satisfies COLA within tol, False otherwise
Notes
In order to enable inversion of an STFT via the inverse STFT in
istft
, the signal windowing must obey the constraint of “Constant OverLap Add” (COLA). This ensures that every point in the input data is equally weighted, thereby avoiding aliasing and allowing full reconstruction. Some examples of windows that satisfy COLA:
 Rectangular window at overlap of 0, 1/2, 2/3, 3/4, ...
 Bartlett window at overlap of 1/2, 3/4, 5/6, ...
 Hann window at 1/2, 2/3, 3/4, ...
 Any Blackman family window at 2/3 overlap
 Any window with
noverlap = nperseg1
A very comprehensive list of other windows may be found in [R243], wherein the COLA condition is satisfied when the “Amplitude Flatness” is unity.
New in version 0.19.0.
References
[R242] Julius O. Smith III, “Spectral Audio Signal Processing”, W3K Publishing, 2011,ISBN 9780974560731. [R243] (1, 2) G. Heinzel, A. Ruediger and R. Schilling, “Spectrum and spectral density estimation by the Discrete Fourier transform (DFT), including a comprehensive list of window functions and some new attop windows”, 2002, http://hdl.handle.net/11858/00001M00000013557A5 Examples
>>> from scipy import signal
Confirm COLA condition for rectangular window of 75% (3/4) overlap:
>>> signal.check_COLA(signal.boxcar(100), 100, 75) True
COLA is not true for 25% (1/4) overlap, though:
>>> signal.check_COLA(signal.boxcar(100), 100, 25) False
“Symmetrical” Hann window (for filter design) is not COLA:
>>> signal.check_COLA(signal.hann(120, sym=True), 120, 60) False
“Periodic” or “DFTeven” Hann window (for FFT analysis) is COLA for overlap of 1/2, 2/3, 3/4, etc.:
>>> signal.check_COLA(signal.hann(120, sym=False), 120, 60) True
>>> signal.check_COLA(signal.hann(120, sym=False), 120, 80) True
>>> signal.check_COLA(signal.hann(120, sym=False), 120, 90) True