Short-Time FFT (DSP Blockset)

Compute a nonparametric estimate of the spectrum using the short-time, fast Fourier transform (ST-FFT) method.

Library

Description

The Short-Time FFT block computes a nonparametric estimate of the spectrum. The block averages the squared magnitude of the FFT computed over windowed sections of the input, and normalizes the spectral average by the square of the sum of the window samples.

Both an M-by-N frame-based matrix input and an M-by-N sample-based matrix input are treated as M sequential time samples from N independent channels. The block computes a separate estimate for each of the N independent channels and generates an N_fft-by-N matrix output. When Inherit FFT length from input dimensions is selected, N_fft is specified by the frame size of the input, which must be a power of 2. When Inherit FFT length from input dimensions is not selected, N_fft is specified as a power of 2 by the FFT length parameter, and the block zero pads or truncates the input to N_fft before computing the FFT.

Each column of the output matrix contains the estimate of the corresponding input column's power spectral density at N_fft equally spaced frequency points in the range [0,F_s), where F_s is the signal's sample frequency. The output is always sample-based.

The Number of spectral averages specifies the number of spectra to average. Setting this parameter to 1 effectively disables averaging.

The Window type, Stopband ripple, Beta, and Window sampling parameters all apply to the specification of the window function; see the reference page for the Window Function block for more details on these four parameters.

Example

The dspstfft demo provides an illustration of using the Short-Time FFT and Matrix Viewer blocks to create a spectrogram. The dspsacomp demo compares the ST-FFT with several other spectral estimation methods.

Dialog Box

Window type: The type of window to apply. (See the Window Function block reference.) Tunable.
Stopband attenuation in dB: The level (dB) of stopband attenuation, R_s, for the Chebyshev window. Disabled for other Window type selections. Tunable.
Beta: The parameter for the Kaiser window. Disabled for other Window type selections. Increasing Beta widens the mainlobe and decreases the amplitude of the window sidelobes in the window's frequency magnitude response. Tunable.
Window sampling: The window sampling, symmetric or periodic. Tunable.
Inherit FFT length from input dimensions: When selected, uses the input frame size as the number of data points, N_fft, on which to perform the FFT.
FFT length: The number of data points, N_fft, on which to perform the FFT. If N_fft exceeds the input frame size, the frame is zero-padded as needed. This parameter is enabled when Inherit FFT length from input dimensions is not selected.
Number of spectral averages: The number of spectra to average; setting this parameter to 1 effectively disables averaging.

References

Oppenheim, A. V. and R. W. Schafer. Discrete-Time Signal Processing. Englewood Cliffs, NJ: Prentice Hall, 1989.

Proakis, J. and D. Manolakis. Digital Signal Processing. 3rd ed. Englewood Cliffs, NJ: Prentice-Hall, 1996.

Supported Data Types

Double-precision floating point
Single-precision floating point

To learn how to convert to the above data types in MATLAB and Simulink, see Supported Data Types and How to Convert to Them.

See Also

Burg Method
DSP Blockset

Magnitude FFT
DSP Blockset

Window Function
DSP Blockset

Spectrum Scope
DSP Blockset

Yule-Walker Method
DSP Blockset

pwelch
Signal Processing Toolbox

Burg Method	DSP Blockset
Magnitude FFT	DSP Blockset
Window Function	DSP Blockset
Spectrum Scope	DSP Blockset
Yule-Walker Method	DSP Blockset
`pwelch`	Signal Processing Toolbox

See Power Spectrum Estimation for related information. Also see a list of all blocks in the Power Spectrum Estimation library.

Sample and Hold Signal From Workspace