# cwtth: Cauchy's wavelet transform In Rwave: Time-Frequency Analysis of 1-D Signals

## Description

Compute the continuous wavelet transform with (complex-valued) Cauchy's wavelet.

## Usage

 `1` ```cwtTh(input, noctave, nvoice=1, moments, twoD=TRUE, plot=TRUE) ```

## Arguments

 `input` input signal (possibly complex-valued). `noctave` number of powers of 2 for the scale variable. `nvoice` number of scales in each octave (i.e. between two consecutive powers of 2). `moments` number of vanishing moments. `twoD` logical variable set to `T` to organize the output as a 2D array (signal size x nb scales), otherwise, the output is a 3D array (signal size x noctave x nvoice). `plot` if set to `T`, display the modulus of the continuous wavelet transform on the graphic device.

## Details

The output contains the (complex) values of the wavelet transform of the input signal. The format of the output can be

2D array (signal size x nb scales)

3D array (signal size x noctave x nvoice)

## Value

 `tmp` continuous (complex) wavelet transform.

## References

See discussions in the text of “Practical Time-Frequency Analysis”.

## See Also

`cwt`, `cwtp`, `DOG`, `gabor`.

## Examples

 ```1 2 3``` ``` x <- 1:512 chirp <- sin(2*pi * (x + 0.002 * (x-256)^2 ) / 16) retChirp <- cwtTh(chirp, noctave=5, nvoice=12, moments=20) ```

Rwave documentation built on May 2, 2019, 9:15 a.m.