Filtering techniques

This framework provides implementations of some noise prediction techniques.

Built-in filtering techniques

Static:

• Wiener Filter (WF)

Adaptive:

• Updating Wiener Fitler (UWF)

• Least-Mean-Squares Filter (LMS)

Non-Linear:

• Experimental non-linear LMS Filter variant (PolynomialLMS)

Minimal example

A minimal example of how the filtering techniques can be used. All techniques follow the same interface concept.

>>> import franc as fnc
>>>
>>> # generate data
>>> n_channel = 2
>>> witness, target = fnc.eval.TestDataGenerator([0.1]*n_channel, rng_seed=123).generate(int(1e5))
>>>
>>> # instantiate the filter and apply it
>>> filt = fnc.filt.LMSFilter(n_filter=128, idx_target=0, n_channel=n_channel)
>>> filt.condition(witness, target)
>>> prediction = filt.apply(witness, target) # check on the data used for conditioning
>>>
>>> # success
>>> round(fnc.eval.rms(target-prediction) / fnc.eval.rms(prediction), 10)
0.0815971935