waveletScattering
Wavelet time scattering
Description
Use the waveletScattering
object to create a network for a
wavelet time scattering decomposition using the Gabor (analytic Morlet) wavelet. The network
uses wavelets and a lowpass scaling function to generate low-variance representations of
real-valued time series data. Wavelet time scattering yields representations insensitive to
translations in the input signal without sacrificing class discriminability. You can use the
representations as inputs to a classifier. You can specify the duration of translation
invariance and the number of wavelet filters per octave. The scattering network also supports
time × channel × batch (T×C×B) inputs.
Creation
Description
creates a wavelet
time scattering network with two filter banks. The first filter bank has a quality factor
of eight wavelets per octave. The second filter bank has a quality factor of one wavelet
per octave. By default, sf
= waveletScatteringwaveletScattering
assumes a signal input length
of 1024 samples. The scale invariance length is 512 samples. By default,
waveletScattering
uses periodic boundary conditions.
creates a network for wavelet scattering, sf
= waveletScattering(Name,Value
)sf
, with properties
specified by one or more Name,Value
pair arguments. Properties can be
specified in any order as Name1,Value1,...,NameN,ValueN
. Enclose each
property name in quotes.
Note
With the exception of OversamplingFactor
, after creation you
cannot change a property value of an existing scattering network. For example, if you
have a network sf
with a SignalLength
of 2000,
you must create a second network sf2
for a signal with 2001
samples. You cannot assign a different SignalLength
to
sf
.
Properties
Object Functions
scatteringTransform | Wavelet 1-D scattering transform |
featureMatrix | Scattering feature matrix |
log | Natural logarithm of scattering transform |
filterbank | Wavelet time scattering filter banks |
littlewoodPaleySum | Littlewood-Paley sum |
scattergram | Visualize scattering or scalogram coefficients |
centerFrequencies | Wavelet scattering bandpass center frequencies |
numorders | Number of scattering orders |
numfilterbanks | Number of scattering filter banks |
numCoefficients | Number of wavelet scattering coefficients |
paths | Scattering network paths |
Examples
More About
References
[1] Andén, Joakim, and Stéphane Mallat. “Deep Scattering Spectrum.” IEEE Transactions on Signal Processing 62, no. 16 (August 2014): 4114–28. https://doi.org/10.1109/TSP.2014.2326991.
[2] Mallat, Stéphane. “Group Invariant Scattering.” Communications on Pure and Applied Mathematics 65, no. 10 (October 2012): 1331–98. https://doi.org/10.1002/cpa.21413.