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# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
# Author: adefossez, 2021
"""
FIR windowed sinc highpass and bandpass filters.
Those are convenience wrappers around the filters defined in `julius.lowpass`.
"""
from typing import Sequence, Optional
import torch
# Import all lowpass filters for consistency.
from .lowpass import lowpass_filter, lowpass_filters, LowPassFilter, LowPassFilters # noqa
from .utils import simple_repr
class HighPassFilters(torch.nn.Module):
"""
Bank of high pass filters. See `julius.lowpass.LowPassFilters` for more
details on the implementation.
Args:
cutoffs (list[float]): list of cutoff frequencies, in [0, 0.5] expressed as `f/f_s` where
f_s is the samplerate and `f` is the cutoff frequency.
The upper limit is 0.5, because a signal sampled at `f_s` contains only
frequencies under `f_s / 2`.
stride (int): how much to decimate the output. Probably not a good idea
to do so with a high pass filters though...
pad (bool): if True, appropriately pad the input with zero over the edge. If `stride=1`,
the output will have the same length as the input.
zeros (float): Number of zero crossings to keep.
Controls the receptive field of the Finite Impulse Response filter.
For filters with low cutoff frequency, e.g. 40Hz at 44.1kHz,
it is a bad idea to set this to a high value.
This is likely appropriate for most use. Lower values
will result in a faster filter, but with a slower attenuation around the
cutoff frequency.
fft (bool or None): if True, uses `julius.fftconv` rather than PyTorch convolutions.
If False, uses PyTorch convolutions. If None, either one will be chosen automatically
depending on the effective filter size.
..warning::
All the filters will use the same filter size, aligned on the lowest
frequency provided. If you combine a lot of filters with very diverse frequencies, it might
be more efficient to split them over multiple modules with similar frequencies.
Shape:
- Input: `[*, T]`
- Output: `[F, *, T']`, with `T'=T` if `pad` is True and `stride` is 1, and
`F` is the numer of cutoff frequencies.
>>> highpass = HighPassFilters([1/4])
>>> x = torch.randn(4, 12, 21, 1024)
>>> list(highpass(x).shape)
[1, 4, 12, 21, 1024]
"""
def __init__(self, cutoffs: Sequence[float], stride: int = 1, pad: bool = True,
zeros: float = 8, fft: Optional[bool] = None):
super().__init__()
self._lowpasses = LowPassFilters(cutoffs, stride, pad, zeros, fft)
@property
def cutoffs(self):
return self._lowpasses.cutoffs
@property
def stride(self):
return self._lowpasses.stride
@property
def pad(self):
return self._lowpasses.pad
@property
def zeros(self):
return self._lowpasses.zeros
@property
def fft(self):
return self._lowpasses.fft
def forward(self, input):
lows = self._lowpasses(input)
# We need to extract the right portion of the input in case
# pad is False or stride > 1
if self.pad:
start, end = 0, input.shape[-1]
else:
start = self._lowpasses.half_size
end = -start
input = input[..., start:end:self.stride]
highs = input - lows
return highs
def __repr__(self):
return simple_repr(self)
class HighPassFilter(torch.nn.Module):
"""
Same as `HighPassFilters` but applies a single high pass filter.
Shape:
- Input: `[*, T]`
- Output: `[*, T']`, with `T'=T` if `pad` is True and `stride` is 1.
>>> highpass = HighPassFilter(1/4, stride=1)
>>> x = torch.randn(4, 124)
>>> list(highpass(x).shape)
[4, 124]
"""
def __init__(self, cutoff: float, stride: int = 1, pad: bool = True,
zeros: float = 8, fft: Optional[bool] = None):
super().__init__()
self._highpasses = HighPassFilters([cutoff], stride, pad, zeros, fft)
@property
def cutoff(self):
return self._highpasses.cutoffs[0]
@property
def stride(self):
return self._highpasses.stride
@property
def pad(self):
return self._highpasses.pad
@property
def zeros(self):
return self._highpasses.zeros
@property
def fft(self):
return self._highpasses.fft
def forward(self, input):
return self._highpasses(input)[0]
def __repr__(self):
return simple_repr(self)
def highpass_filters(input: torch.Tensor, cutoffs: Sequence[float],
stride: int = 1, pad: bool = True,
zeros: float = 8, fft: Optional[bool] = None):
"""
Functional version of `HighPassFilters`, refer to this class for more information.
"""
return HighPassFilters(cutoffs, stride, pad, zeros, fft).to(input)(input)
def highpass_filter(input: torch.Tensor, cutoff: float,
stride: int = 1, pad: bool = True,
zeros: float = 8, fft: Optional[bool] = None):
"""
Functional version of `HighPassFilter`, refer to this class for more information.
Output will not have a dimension inserted in the front.
"""
return highpass_filters(input, [cutoff], stride, pad, zeros, fft)[0]
class BandPassFilter(torch.nn.Module):
"""
Single band pass filter, implemented as a the difference of two lowpass filters.
Args:
cutoff_low (float): lower cutoff frequency, in [0, 0.5] expressed as `f/f_s` where
f_s is the samplerate and `f` is the cutoff frequency.
The upper limit is 0.5, because a signal sampled at `f_s` contains only
frequencies under `f_s / 2`.
cutoff_high (float): higher cutoff frequency, in [0, 0.5] expressed as `f/f_s`.
This must be higher than cutoff_high. Note that due to the fact
that filter are not perfect, the output will be non zero even if
cutoff_high == cutoff_low.
stride (int): how much to decimate the output.
pad (bool): if True, appropriately pad the input with zero over the edge. If `stride=1`,
the output will have the same length as the input.
zeros (float): Number of zero crossings to keep.
Controls the receptive field of the Finite Impulse Response filter.
For filters with low cutoff frequency, e.g. 40Hz at 44.1kHz,
it is a bad idea to set this to a high value.
This is likely appropriate for most use. Lower values
will result in a faster filter, but with a slower attenuation around the
cutoff frequency.
fft (bool or None): if True, uses `julius.fftconv` rather than PyTorch convolutions.
If False, uses PyTorch convolutions. If None, either one will be chosen automatically
depending on the effective filter size.
Shape:
- Input: `[*, T]`
- Output: `[*, T']`, with `T'=T` if `pad` is True and `stride` is 1.
..Note:: There is no BandPassFilters (bank of bandpasses) because its
signification would be the same as `julius.bands.SplitBands`.
>>> bandpass = BandPassFilter(1/4, 1/3)
>>> x = torch.randn(4, 12, 21, 1024)
>>> list(bandpass(x).shape)
[4, 12, 21, 1024]
"""
def __init__(self, cutoff_low: float, cutoff_high: float, stride: int = 1, pad: bool = True,
zeros: float = 8, fft: Optional[bool] = None):
super().__init__()
if cutoff_low > cutoff_high:
raise ValueError(f"Lower cutoff {cutoff_low} should be less than "
f"higher cutoff {cutoff_high}.")
self._lowpasses = LowPassFilters([cutoff_low, cutoff_high], stride, pad, zeros, fft)
@property
def cutoff_low(self):
return self._lowpasses.cutoffs[0]
@property
def cutoff_high(self):
return self._lowpasses.cutoffs[1]
@property
def stride(self):
return self._lowpasses.stride
@property
def pad(self):
return self._lowpasses.pad
@property
def zeros(self):
return self._lowpasses.zeros
@property
def fft(self):
return self._lowpasses.fft
def forward(self, input):
lows = self._lowpasses(input)
return lows[1] - lows[0]
def __repr__(self):
return simple_repr(self)
def bandpass_filter(input: torch.Tensor, cutoff_low: float, cutoff_high: float,
stride: int = 1, pad: bool = True,
zeros: float = 8, fft: Optional[bool] = None):
"""
Functional version of `BandPassfilter`, refer to this class for more information.
Output will not have a dimension inserted in the front.
"""
return BandPassFilter(cutoff_low, cutoff_high, stride, pad, zeros, fft).to(input)(input)
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