# wsst

Wavelet synchrosqueezed transform

## Syntax

## Description

returns
the wavelet synchrosqueezed transform, `sst`

= wsst(`x`

)`sst`

, which
you use to examine data in the time-frequency plane. The synchrosqueezed
transform has reduced energy smearing when compared to the continuous
wavelet transform. The input, `x`

, must be a 1-D
real-valued signal with at least four samples. `wsst`

computes
the synchrosqueezed transform using the analytic Morlet wavelet.

`[___] = wsst(`

uses
a `x`

,`ts`

)`duration`

`ts`

with
a positive, scalar input, as the sampling interval. The duration can
be in years, days, hours, minutes, or seconds. If you specify `ts`

and
the `f`

output, `wsst`

returns
the frequencies in `f`

in cycles per unit time,
where the time unit is derived from specified duration.

`[___] = wsst(___,`

uses
the analytic wavelet specified by `wav`

)`wav`

to compute
the synchrosqueezed transform. Valid values are `'amor'`

and `'bump'`

,
which specify the analytic Morlet and bump wavelet, respectively.

`wsst(___)`

with no output arguments plots the synchrosqueezed
transform as a function of time and frequency. If you do not specify a sampling
frequency, `fs`

, or interval, `ts`

, the
synchrosqueezed transform is plotted in cycles per sample. If you specify a sampling
frequency, the synchrosqueezed transform is plotted in Hz. If you specify a sampling
interval using a duration, the plot is in cycles per unit time. The time units are
derived from the duration.

`[___] = wsst(___,`

returns
the synchrosqueezed transform with additional options specified by
one or more `Name,Value`

)`Name,Value`

pair arguments.

## Examples

## Input Arguments

## Output Arguments

## References

[1] Daubechies, I., J. Lu, and H.-T. Wu. "Synchrosqueezed wavelet transforms: an empirical
mode decomposition-like tool." *Applied and Computational Harmonic
Analysis*. Vol. 30, Number 2, 2011, pp. 243–261.

[2] Thakur, G., E. Brevdo, N. S. Fučkar, and H.-T. Wu. "The Synchrosqueezing algorithm for
time-varying spectral analysis: robustness properties and new paleoclimate applications."
*Signal Processing*. Vol. 93, Number 5, 2013, pp.
1079–1094.

## Version History

**Introduced in R2016a**