Stft

Module name: Stft

Package: CEAMSModules 7.4.0

Version: 2.1.0

Overview

Compute the STFT (right-side only) on the signal split into sliding windows. Different normalizations of the FFT output are available. The normalization determines how the spectral values should be interpreted (sinusoidal amplitude estimation, integrated spectral power, or spectral density).

Inputs

Input

Format

Default

Description

signals

a list of SignalModel

—
signal.samples : The actual signal data as numpy list
signal.sample_rate : sampling rate of the signal (used for STFT)
signal.channel : current channel label

win_len_sec

float

1

window length in seconds (amount of data used for each FFT)

win_step_sec

float

1

window step in seconds (interval between successive FFT computations)

zeros_pad

bool, optional

False
Zero-pad the data to the next fast FFT size.
Zero padding increases the apparent frequency resolution and can
reduce computation time depending on the FFT size. Padding to the
next power of two is not always optimal.
(default = False)

window_name

string, optional

hann

Name of the window applied to the time series before computing the FFT.

rm_mean

bool, optional

True
Remove the mean of each window prior to the FFT process
(default = True)

norm

string, optional

integrate
Normalization applied to the FFT output.
(default = integrate)
“integrate” :
Normalization preserving the total signal power (energy).
This correction compensates for the attenuation introduced by
the window using the window noise gain. Each frequency bin
represents spectral power (units²/bin, e.g. µV²/bin).
Summing bins over a frequency range yields the total band power:
µV²/bin → sum over bins → µV²
“rms” :
Normalization preserving the amplitude of sinusoidal components.
This uses the coherent gain of the window so that the RMS amplitude
of sinusoidal signals is correctly represented in the spectrum.
Each bin represents the discrete spectral power associated with
that frequency component (units²/bin, e.g. µV²/bin).
“noise” :
Power spectral density (PSD) normalization suitable for estimating
broadband noise levels. The spectrum is expressed as a density
per frequency unit:
units²/Hz (e.g. µV²/Hz)
“no” :
No normalization applied to the FFT output.

filename

string

—
Python filename (including path) used to save the STFT cache
in order to navigate through epochs.

Outputs

Output

Format

Description

psd

list of dicts
keys of the dict:
psd : spectral power values
freq_bins : frequency bins (Hz)
win_len : window length (s)
win_step : window step (s)
sample_rate : sampling rate of the original signal (Hz)
chan_label : channel label

Usage in a process

  1. Open Dev Tools -> New process in Snooz.

  2. In the Module Library, find Stft under the Signal Processing category.

  3. Drag the module onto the process canvas.

  4. Connect the required inputs from upstream modules (or set values in the Settings tab).

  5. Connect outputs to downstream modules as needed.

  6. Double-click the module to configure parameters in the Settings tab.

  7. Run the process and inspect results in the Results tab.

Note

For general guidance on building processes with modules, see Explore examples.