gives the continuous wavelet transform of a list of values xi.


gives the continuous wavelet transform using the wavelet wave.


gives the continuous wavelet transform using noct octaves with nvoc voices per octave.


gives the continuous wavelet transform of sampled sound.

Details and Options


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Basic Examples  (2)

Compute a continuous wavelet transform using MexicanHatWavelet:

Plot the coefficients:

Perform an inverse continuous wavelet transform:

Transform a sampled Sound object:

Plot a scalogram:

Scope  (18)

Basic Uses  (6)

Compute a continuous wavelet transform:

Show all the voices for the 8^(th) octave:

Use Normal to get all wavelet coefficients explicitly:

Also use All as an argument to get all coefficients:

Use "IndexMap" to find out what wavelet coefficients are available:

Extract specific coefficient arrays:

Extract several wavelet coefficients corresponding to the list of wavelet index specifications:

Extract all coefficients whose wavelet indexes match a pattern:

WaveletScalogram gives a time scale representation of wavelet coefficients:

More voices per octave increases the scale resolution:

Higher number of octaves gives a wider spectrum of scale range:

Time and Scale Features  (4)

A single frequency shows up as a horizontal band at the equivalent scale:

Multiple frequencies show up as multiple bands at the equivalent scales:

Sinusoid with linearly increasing frequency:

Wavelet transform gives a good time localization of features:

Higher frequencies are resolved at lower octaves and lower frequencies at higher octaves:

Resolve time and frequency features of a signal:

Use GaborWavelet to perform a continuous wavelet transform:

There is an inverse relationship between scale values and frequency values:

Find pairs of {oct,voc} that resolve frequencies 20 Hz and 70 Hz:

Verify using a WaveletScalogram:

Wavelet Families  (6)

Compute the wavelet transform using different wavelet families:

A narrow wavelet function will have good time and scale resolution:

A broad wavelet function will have poor time and scale resolution:

Use different families of wavelets to capture different features:

MexicanHatWavelet (default):





Sound  (2)

ContinuousWaveletTransform works on Sound as input:

Speech analysis using ContinuousWaveletTransform:

The orange patches correspond to the words "You will return safely to Earth":

Extract octaves 5 and 6:

Options  (9)

Padding  (3)

The settings for Padding are the same as the methods for ArrayPad, including "Periodic":







Padding has no effect on the length of wavelet coefficients:

Padding pads the input data to the next higher power of 2 to reduce boundary effects:

Boundary effects at the start:

Boundary effects at the end:

SampleRate  (3)

For lists, the Automatic value of SampleRate is set to 1:

Explicitly set the sample rate:

For Sound data, the Automatic value of SampleRate is extracted from the Sound data object:

SampleRate is used for normalizing wavelet transform coefficients:

WaveletScale  (1)

WaveletScale indicates the smallest resolvable scale used for the transform:

The scales used are given as with wavelet scale, octave, and voice:

WorkingPrecision  (2)

By default, WorkingPrecision->MachinePrecision is used:

Use higher-precision computation:

Applications  (4)

Identify Features  (2)

Real wavelet functions can be used to isolate peaks or discontinuities:

Complex wavelets can be used to capture oscillatory behavior:

Amplitude of wavelet coefficients:

Phase of wavelet coefficients:

Filter Frequencies  (2)

ContinuousWaveletTransform can be used to filter frequencies:

Filter the cosine with frequency :

Perform InverseContinuousWaveletTransform on a thresholded data object:

The final filtered signal:

Identify musical notes using a scalogram:

Generate a sequence of pitches corresponding to an equal-tempered scale at 300 Hz:

Compute frequencies resolved corresponding to octaves and voices:

Find pairs of {oct,voc} that resolve frequencies 300 Hz:

Properties & Relations  (1)

The default value for octave is given by TemplateBox[{{InterpretationBox[{log, _, DocumentationBuild`Utils`Private`Parenth[2]}, Log2, AutoDelete -> True], (, {n, /, 2}, )}}, Floor]:

Default value of voices is 4:

Possible Issues  (1)

Low-frequency data is resolved at higher octaves:

Based on the length of input data, the Automatic setting for octaves resolved 8 octaves:

Increase the number of octaves to resolve the low-frequency component:

Neat Examples  (1)

Scalogram of a Zeta function:

Wolfram Research (2010), ContinuousWaveletTransform, Wolfram Language function,


Wolfram Research (2010), ContinuousWaveletTransform, Wolfram Language function,


Wolfram Language. 2010. "ContinuousWaveletTransform." Wolfram Language & System Documentation Center. Wolfram Research.


Wolfram Language. (2010). ContinuousWaveletTransform. Wolfram Language & System Documentation Center. Retrieved from


@misc{reference.wolfram_2024_continuouswavelettransform, author="Wolfram Research", title="{ContinuousWaveletTransform}", year="2010", howpublished="\url{}", note=[Accessed: 12-June-2024 ]}


@online{reference.wolfram_2024_continuouswavelettransform, organization={Wolfram Research}, title={ContinuousWaveletTransform}, year={2010}, url={}, note=[Accessed: 12-June-2024 ]}