# icwtLayer

## Description

An ICWT layer computes the inverse continuous wavelet transform of the input. You must have Deep Learning Toolbox™ to use this layer.

## Creation

### Description

creates an inverse
continuous wavelet transform (ICWT) layer.`layer`

= icwtLayer

The input to `icwtLayer`

must be a real-valued `dlarray`

(Deep Learning Toolbox) object in
the `"CBT"`

or `"SCBT"`

format. The size of the channel
(`"C"`

) dimension must be even, because `icwtLayer`

assumes the real and imaginary parts of the input are concatenated along the channel
dimension.

The output of `icwtLayer`

is real-valued and in
`"CBT"`

format.

**Note**

`icwtLayer`

initializes the weights internally to be the wavelet
filters used in the CWT. Initializing the weights directly is not recommended.

creates an ICWT layer with properties specified by one or
more name-value arguments. For example, `layer`

= icwtLayer(`Name=Value`

)```
layer =
icwtLayer(SignalLengh=2048,VoicesPerOctave=14)
```

creates a layer for a signal of
length 2048 in the time dimension that uses 14 voices per octave in the ICWT. You can
specify multiple name-value arguments.

## Properties

## Object Functions

`filterbank` | Full-weight CWT filter bank for deep learning |

## Examples

## Tips

For the best reconstruction when the filters in

`icwtLayer`

and`cwtLayer`

are not learnable (which is the default setting):Ensure consistency in the filter bank used for the CWT and ICWT by using the same parameters in

`icwtLayer`

and`cwtLayer`

.Include the scaling coefficients by setting

`IncludeLowpass`

to`true`

in`icwtLayer`

and`cwtLayer`

.

## Version History

**Introduced in R2024b**

## See Also

### Apps

- Deep Network Designer (Deep Learning Toolbox)

### Functions

`dlcwt`

|`dlicwt`

|`cwt`

|`icwt`

|`cwtfreqbounds`

|`cwtfilters2array`

|`array2cwtfilters`

### Objects

`cwtLayer`

|`cwtfilterbank`

|`dlarray`

(Deep Learning Toolbox) |`dlnetwork`

(Deep Learning Toolbox)

### Topics

- Practical Introduction to Time-Frequency Analysis Using the Continuous Wavelet Transform
- Time-Frequency Convolutional Network for EEG Data Classification
- Time-Frequency Feature Embedding with Deep Metric Learning
- Deep Learning in MATLAB (Deep Learning Toolbox)
- List of Deep Learning Layers (Deep Learning Toolbox)