scipy.signal.check_COLA

scipy.signal.check_COLA(window, nperseg, noverlap, tol=1e-10)

Check whether the Constant OverLap Add (COLA) constraint is met

Parameters:

window : str or tuple or array_like

Desired window to use. See get_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

See also

stft

Short Time Fourier Transform

istft

Inverse Short Time Fourier Transform

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 = nperseg-1

A very comprehensive list of other windows may be found in [R207], wherein the COLA condition is satisfied when the “Amplitude Flatness” is unity.

New in version 0.19.0.

References

[R206]

Julius O. Smith III, “Spectral Audio Signal Processing”, W3K Publishing, 2011,ISBN 978-0-9745607-3-1.

[R207]

(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 at-top windows”, 2002, http://hdl.handle.net/11858/00-001M-0000-0013-557A-5

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 “DFT-even” 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