# cwtmag2sig

## Syntax

## Description

returns a time-domain real-valued signal `x`

= cwtmag2sig(`cfs`

)`x`

estimated from the
continuous wavelet transform (CWT) magnitude, `cfs`

, using the gradient
descent method and the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimizer.
By default, `cwtmag2sig`

uses the Morse (3,60) wavelet with default
frequency range. The function assumes:

The CWT magnitude was obtained from a real-valued signal.

The

`ExtendSignal`

of the original CWT is`false`

or the Boundary of the`cwtfilterbank`

object used in the original CWT is`"periodic"`

.

To use the `cwtmag2sig`

function, you must have a Deep Learning Toolbox™ license.

specifies additional options using name-value arguments. Options include, among others, the
analytic wavelet to use in the reconstruction and the method to specify initial phases.
These arguments can be added to any of the previous input syntaxes. For example,
`x`

= cwtmag2sig(___,`Name=Value`

)`Wavelet="bump",InitializePhaseMethod="random"`

specifies that the signal
is estimated using the bump wavelet with random initial phases.

For optimal reconstruction results, the sample rate and the values of the CWT Parameters must be consistent with those used to obtain the CWT magnitude. Also, retain and reuse the scaling coefficients obtained from the original CWT.

## Examples

### Reconstruct Sinusoid from CWT Magnitude

Consider a sinusoid with a frequency of 2 Hz and a DC value of 1. Sample the signal at 1000 Hz. Compute the CWT of the signal. In the CWT, do not extend the signal symmetrically. Also obtain the scaling coefficients of the transform.

fs = 1000; ts = 0:1/fs:2-1/fs; x = cos(2*pi*2*ts).'+1; [CFS,~,~,~,scalcfs] = cwt(x,ExtendSignal=false);

Reconstruct the sinusoid from the magnitude of the CWT. Display the normalized inconsistency during the reconstruction process.

```
xrec = cwtmag2sig(abs(CFS),...
Display=true,ScalingCoefficients=scalcfs);
```

#Iteration | Normalized Inconsistency 1 | 1.6552e+00 20 | 2.5206e-03 40 | 6.9620e-03 60 | 7.6927e-04 67 | 1.5159e-06 Decomposition process stopped. The normalized inconsistency for each channel is smaller than the "InconsistencyTolerance" of 1e-05.

Plot the original and reconstructed signals.

plot(ts,x,ts,xrec,"--") xlabel("Time (s)") ylabel("Amplitude") legend("Original","Reconstructed")

### Use CWT Filter Bank to Reconstruct Multichannel Signal from CWT Magnitude

Load an ECG signal corresponding to record 200 of the MIT-BIH Arrhythmia Database. Extract two segments, each with 1000 samples, from the signal. The workspace variable `tm`

contains the sample times.

```
load mit200
idx1 = 1:1000;
idx2 = 3001:4000;
x1 = ecgsig(idx1);
x2 = ecgsig(idx2);
```

Create a CWT filter bank that you can use on the segments. Specify periodic boundary handling. Use the filter bank to obtain the CWT of the segments. Also obtain the scaling coefficients.

cfb = cwtfilterbank(SignalLength=length(x1), ... Boundary="periodic"); [CFS1,~,~,~,d1] = cwt(x1,Filterbank=cfb); [CFS2,~,~,~,d2] = cwt(x2,Filterbank=cfb);

Concatenate the CWT coefficients along the third dimension. Also concatenate the scaling coefficients along the third dimension. Concatenate the segments along the second dimension. Treat each segment as a separate channel in a multichannel signal.

CFS = cat(3,CFS1,CFS2); d = cat(3,d1,d2); x = cat(2,x1,x2);

Use the filter bank to reconstruct the multichannel signal from the magnitudes of the CWT. Display the normalized inconsistency during the reconstruction process.

```
xrec = cwtmag2sig(abs(CFS),Display=true, ...
ScalingCoefficients=d,Filterbank=cfb);
```

#Iteration | Normalized Inconsistency 1 | 1.6683e+00 1.6607e+00 20 | 1.3074e-01 2.0297e-01 40 | 4.0472e-02 4.1970e-02 60 | 6.8938e-03 4.8925e-03 80 | 1.8965e-02 2.0714e-02 100 | 5.7284e-02 3.3896e-02 120 | 4.1591e-02 1.3019e-02 140 | 5.0762e-02 5.6419e-03 160 | 1.9428e-02 3.9582e-04 180 | 1.9712e-03 2.5361e-05 200 | 2.9577e-04 5.7740e-06 220 | 1.1951e-05 5.7740e-06 222 | 9.2158e-06 5.7740e-06 Decomposition process stopped. The normalized inconsistency for each channel is smaller than the "InconsistencyTolerance" of 1e-05.

Plot the original and reconstructed channels.

tiledlayout(2,1) nexttile plot(tm(idx1),x(:,1),tm(idx1),xrec(:,1),"--") ylabel("Amplitude") title("First Channel") legend("Original","Reconstructed",Location="northoutside") nexttile plot(tm(idx2),x(:,2),tm(idx2),xrec(:,2),"--") title("Second Channel") ylabel("Amplitude") xlabel("Time (s)")

## Input Arguments

`cfs`

— CWT magnitude

matrix | 3-D array

CWT magnitude, specified as a matrix or a 3-D array. `cfs`

must
correspond to a real-valued signal.

When

`cfs`

corresponds to a single-channel signal, specify`cfs`

as a matrix, with time increasing across the columns and frequency decreasing down the rows.When

`cfs`

corresponds to a multichannel signal, specify`cfs`

as a 3-D array, where the third dimension corresponds to the channels.

**Data Types: **`single`

| `double`

`fs`

— Sample rate

scalar

Sample rate corresponding to `x`

in hertz, specified as a
positive scalar.

**Data Types: **`single`

| `double`

`ts`

— Sample period

scalar duration

Sample period corresponding to `x`

, specified as a positive
scalar duration. The sample rate is calculated as `1/ts`

.

**Data Types: **`duration`

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

**Example: **`Wavelet="amor",VoicesPerOctave=20,Optimizer="adam"`

specifies
that the signal is estimated using the Morlet (Gabor) wavelet, 20 voices per octave, and the
adaptive moment estimation (ADAM) optimizer.

**CWT Parameters**

`FrequencyLimits`

— Frequency limits

two-element scalar vector

Frequency limits used for reconstruction, specified as a two-element vector with positive strictly increasing entries.

The first element specifies the lowest peak passband frequency and must be greater than or equal to the product of the wavelet peak frequency in hertz and two time standard deviations divided by the signal length.

The second element specifies the highest peak passband frequency and must be less than or equal to the Nyquist frequency.

The base-2 logarithm of the ratio of the upper frequency limit,
`freqMax`

, to the lower frequency limit,
`freqMin`

, must be greater than or equal to `1/NV`

,
where `NV`

is the number of voices per octave:

`log`

._{2}(freqMax/freqMin) ≥
1/NV

If you specify frequency limits outside the permissible range,
`cwtmag2sig`

truncates the limits to the minimum and maximum
valid values.

**Data Types: **`single`

| `double`

`PeriodLimits`

— Period limits

two-element duration array

Period limits to use in the CWT, specified as a two-element duration array with strictly increasing positive entries.

The first element must be greater than or equal to 2 ×

`ts`

, where`ts`

is the sampling period.The maximum period cannot exceed the signal length divided by the product of two time standard deviations of the wavelet and the wavelet peak frequency.

The base-2 logarithm of the ratio of the minimum period,
`minP`

, to the maximum period, `maxP`

, must be
less than or equal to `-1/NV`

, where `NV`

is the
number of voices per octave:

`log`

._{2}(pMin/pMax) ≤
-1/NV

If you specify period limits outside the permissible range,
`cwtmag2sig`

truncates the limits to the minimum and maximum
valid values.

**Data Types: **`duration`

`Wavelet`

— Wavelet

`"morse"`

(default) | `"amor"`

| `"bump"`

Wavelet used for reconstruction, specified as `"morse"`

,
`"amor"`

, or `"bump"`

. These strings specify the
analytic Morse, Morlet (Gabor), and bump wavelet, respectively. The default wavelet is
the analytic Morse (3,60) wavelet. To learn more about Morse wavelets, see Morse Wavelets.

**Data Types: **`char`

| `string`

`VoicesPerOctave`

— Number of voices per octave

`10`

(default) | integer

Number of voices per octave used for reconstruction, specified as a positive integer between 1 and 48. The CWT scales are discretized using the specified number of voices per octave.

**Data Types: **`single`

| `double`

`TimeBandwidth`

— Time-bandwidth product of the Morse wavelet

`60`

(default) | scalar greater than or equal to 3 and less than or equal to 120

Time-bandwidth product for the Morse wavelet used for reconstruction, specified as
a positive scalar greater than or equal to 3 and less than or equal to 120. The
symmetry (gamma) of the Morse wavelet is fixed at 3. This property is only valid when
`Wavelet`

is `"morse"`

.

You cannot specify both `TimeBandwidth`

and
`WaveletParameters`

.

In the notation of Morse Wavelets,
`TimeBandwidth`

is
*P*^{2}.

**Data Types: **`single`

| `double`

`WaveletParameters`

— Morse wavelet parameters

`[3,60]`

(default) | two-element vector of scalars

Morse wavelet parameters used for reconstruction, specified as a two-element
vector. The first element is the symmetry parameter (gamma), which must be greater
than or equal to 1. The second element is the time-bandwidth product, which must be
greater than or equal to gamma. The ratio of the time-bandwidth product to gamma
cannot exceed 40. This property is only valid when `Wavelet`

is
`"morse"`

.

You cannot specify both `WaveletParameters`

and
`TimeBandwidth`

.

**Data Types: **`single`

| `double`

`FilterBank`

— CWT filter bank

`cwtfilterbank`

object

CWT filter bank used for reconstruction, specified as a
`cwtfilterbank`

object. The CWT filter bank must have
`Boundary="periodic"`

. If you specify
`FilterBank`

, you cannot specify any other CWT parameters. All
options for the computation of the CWT are defined as properties of the filter bank.
For more information, see `cwtfilterbank`

.

**Low Frequency Information**

`ScalingCoefficients`

— Scaling coefficients

vector | 3-D array

Scaling coefficients for reconstruction, specified as a vector or 3-D array. You
can obtain the scaling coefficients as an optional output of the `cwt`

function.

For a single-channel signal, specify the scaling coefficients as a real-valued vector that is the same length as the column size of

`cfs`

.For a multichannel signal, concatenate the scaling coefficients of each channel along the third dimension, and specify the scaling coefficients as the resulting 3-D array.

You cannot specify both `ScalingCoefficients`

and
`SignalMean`

.

**Data Types: **`single`

| `double`

`SignalMean`

— Signal mean

scalar | vector

Signal mean to add to the reconstruction, specified as a scalar or vector. If the
signal mean is a vector, it must be the same length as the column size of the wavelet
coefficient matrix `cfs`

.

For a single-channel signal, specify the signal mean as a real-valued scalar.

For a multichannel signal, specify the signal mean as a real-valued vector, where the

*i*th element of`SignalMean`

is the mean of the*i*the channel.

Because the CWT does not preserve the signal mean, the reconstructed signal is a zero-mean signal by default. Adding a non-zero mean to a frequency- or period-limited reconstruction adds a zero-frequency component to the reconstruction.

You cannot specify both `SignalMean`

and
`ScalingCoefficients`

.

**Data Types: **`single`

| `double`

**Algorithm Parameters**

`InitializePhaseMethod`

— CWT phase initialization

`"random"`

(default) | `"zeros"`

CWT phase initialization, specified as `"random"`

or
`"zeros"`

. Specify only one of
`InitializePhaseMethod`

or `InitialPhase`

.

`"random"`

— The function initializes the CWT phases as random numbers distributed uniformly in the interval [–*π*,*π*].`"zeros"`

— The function initializes the CWT phases as zeros.

**Data Types: **`char`

| `string`

`InitialPhase`

— Initial CWT phases

matrix | 3-D array

Initial CWT phases, specified as a real numeric matrix or 3-D array. Elements of
`InitialPhase`

must be in the range [–*π*, *π*]. `InitialPhase`

must have the same size as
`cfs`

. Specify only one of
`InitializePhaseMethod`

or
`InitialPhase`

.

**Data Types: **`single`

| `double`

`MaxIterations`

— Maximum number of optimization iterations

`300`

(default) | positive integer

Maximum number of optimization iterations used in the reconstruction process,
specified as a positive integer. The reconstruction process stops when the number of
iterations is greater than `MaxIterations`

.

**Data Types: **`single`

| `double`

`InconsistencyTolerance`

— Inconsistency tolerance of reconstruction process

`1e-5`

(default) | positive scalar

Inconsistency tolerance of reconstruction process, specified as a positive scalar. The reconstruction process stops when the Normalized Inconsistency is lower than the tolerance.

**Data Types: **`single`

| `double`

`Optimizer`

— Optimizer used in the gradient descent method

`"lbfgs"`

(default) | `"sgdm"`

| `"adam"`

| `"rmsprop"`

Optimizer used in the gradient descent method, specified as one of these:

`"lbfgs"`

— Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimizer`"sgdm"`

— Stochastic gradient descent with momentum (SGDM) optimizer`"adam"`

— Adaptive moment estimation (ADAM) optimizer`"rmsprop"`

— Root-mean-square propagation (RMSProp) optimizer

**Data Types: **`char`

| `string`

`StepSize`

— Step size for iterative updates

`0.01`

(default) | positive scalar

Step size for iterative updates used in the gradient descent method, specified as
a positive scalar. This argument is valid only when `Optimizer`

is
not `"lbfgs"`

.

**Data Types: **`single`

| `double`

`Display`

— Inconsistency display option

`false`

or `0`

(default) | `true`

or `1`

Inconsistency display option, specified as a numeric or logical
`1`

(`true`

) or `0`

(`false`

). If this option is set to `true`

,
`cwtmag2sig`

displays the normalized inconsistency after every 20
optimization iterations. The function also displays stopping information at the end of
the run.

**Data Types: **`logical`

## Output Arguments

`x`

— Estimated time-domain signal

vector | matrix

Estimated time-domain signal, returned as a vector or a matrix.

`t`

— Time instants

vector

Time instants at which the signal is reconstructed, returned as a vector. When a
sample rate is provided, `t`

is a numeric vector in seconds. When a
sample time duration `ts`

is provided, `t`

is a
duration array with the same unit as `ts`

. `t`

is
a numeric vector in sample numbers if no time information is provided. The number of
rows in `x`

equals the length of `t`

.

`info`

— Reconstruction process information

structure

Reconstruction process information, returned as a structure containing these fields:

`ExitFlag`

— Termination flag of each channel.A value of

`0`

indicates the algorithm stopped when it reached the maximum number of iterations.A value of

`1`

indicates the algorithm stopped when it met the relative tolerance.

For multichannel signals,

`ExitFlag`

is a vector.`NumIterations`

— Total number of iterations for each channel. For multichannel signals,`NumIterations`

is a vector.`Inconsistency`

— Average relative improvement toward convergence between the last two iterations for each channel. For multichannel signals,`Inconsistency`

is a vector.`ReconstructedCWT`

— Reconstructed CWT at the final iteration.`ReconstructedPhase`

— Reconstructed phase at the final iteration.

## More About

### Normalized Inconsistency

*Normalized inconsistency* measures improvement
toward convergence of the reconstruction process in successive optimization
iterations.

Normalized inconsistency is defined as

$$\text{Inconsistency}=\frac{\Vert {s}_{\text{est},i}-{s}_{\text{est},i-1}\Vert}{\text{cfs}},$$

where *s*_{est,i} is the complex CWT estimated at the *i*th iteration and
the brackets denote the matrix norm.

`cwtmag2sig`

uses the MATLAB^{®} function `norm`

to compute matrix norms.

## Tips

If the reconstruction is not satisfactory, set

`Display`

to`true`

. Observe the inconsistency during iterations. If the inconsistency does not decrease, reduce`StepSize`

for better reconstruction.The L-BFGS algorithm generally requires the fewest iterations and yields the best reconstruction results. This optimizer automatically selects the step size for each iteration. However, particularly when processing batch data inputs, the computation speed can be slow. When you need to perform a large number of calculations quickly, other optimizers are recommended.

## Extended Capabilities

### GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

## Version History

**Introduced in R2023b**

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