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Filter Classes#

The following introduces the concept of the pyfar filter classes. See filter classes for a complete documentation.

Types#

There are three types of Filter objects:

  • FIR Filter: Finite Impulse Response (FIR) filters are non-recursive filters. FIR filters are very flexible and can have arbitrary magnitude and phase responses.

  • IIR Filter: Infinite Impulse Response (IIR) filters are recursive filters. They can achieve steeper filter slopes than FIR filters of the same order but are less flexible with respect to the phase response.

  • SOS Filter: Second Order Section (SOS) filters are cascaded 2nd order recursive filters. They are often more robust against numerical errors than IIR filters of the same order.

Initializing a Filter Object#

A filter object is initialized at least with the coefficients and a sampling rate

import pyfar as pf
filter = pf.FilterFIR([[3, -1]], 44100)

Applying a time-invariant Filter#

To filter an audio signal, pass it to the filters process function

input = pf.Signal([1, 2, 3, 4], 44100)
output = filter.process(input)

The output is output.time = [[3, 5, 7, 9]] and has the same number of samples as the input.

The output will be the same no matter how often process is called. This default behavior is often desired. In some cases, a different functionality can be useful. For blockwise processing of input signals, the filter object can track the state of the filter. The initial state can be passed during initialization or typical states can be set using

filter.init_state(input.cshape, state='zeros')

The above initializes the state with zeros, and if the filter is called with blocks of the input

block_one = filter.process(input[0, 0:2])
block_two = filter.process(input[0, 2:4])

the blockwise output yields the same as the complete output output seen above, i.e., block_one.time = [[3, 5]], block_two.time = [[7, 9]]. This is the case because initializing the state also makes the filter object track the state across multiple calls of the process functions.

Another option for initialization is

filter.init_state(input.cshape, state='step')

which makes sure that the first sample of the output is the same as the first sample of the input.

To disable tracking the state, call process with reset=True

output = filter.process(input, reset=True)

or simply do not initialize the state at all as done by

filter = pf.FilterFIR([[3, -1]], 44100, state=None)

which is the default.

Applying a time-variant Filter#

In some cases it is necessary to mimic time-variant systems. This can be done by exchanging the filter coefficients

# input signal
input = pf.Signal([1, 2, 3, 4], 44100)

# initialize filter and state
filter = pf.FilterFIR([[2, -2]], 44100)
filter.init_state(input.cshape, state='zeros')

# process first block
output_1 = filter.process(input[..., :2])

# update filter coefficients
filter.coefficients = [[2, -1.9]]

# process second block
output_2 = filter.process(input[..., 2:])

Note that this only works for filters of the same length and after initializing the state. Otherwise, discontinuities will appear between the output blocks.

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