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Wavelet synchrosqueezed transform

`sst = wsst(x)`

```
[sst,f]
= wsst(x)
```

`[___] = wsst(x,fs)`

`[___] = wsst(x,ts)`

`[___] = wsst(___,wav)`

`wsst(___)`

`[___] = wsst(___,Name,Value)`

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.

[1] I. Daubechies, I., J. Lu, and H. T. Wu. "Synchrosqueezed
Wavelet Transforms: an Empricial Mode Decomposition-like Tool", *Applied
and Computational Harmonic Analysis*. Vol. 30(2), 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, pp. 1079–1094.

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