Module julius.lowpass
FIR windowed sinc lowpass filters.
Expand source code Browse git
# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
# Author: adefossez, 2020
"""
FIR windowed sinc lowpass filters.
"""
import math
from typing import Sequence, Optional
import torch
from torch.nn import functional as F
from .core import sinc
from .fftconv import fft_conv1d
from .utils import simple_repr
class LowPassFilters(torch.nn.Module):
"""
Bank of low pass filters. Note that a high pass or band pass filter can easily
be implemented by substracting a same signal processed with low pass filters with different
frequencies (see `julius.bands.SplitBands` for instance).
This uses a windowed sinc filter, very similar to the one used in
`julius.resample`. However, because we do not change the sample rate here,
this filter can be much more efficiently implemented using the FFT convolution from
`julius.fftconv`.
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. Keep in mind that decimation
of the output is only acceptable if the cutoff frequency is under `1/ (2 * stride)`
of the original sampling rate.
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 lowpass 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.
..note::
A lowpass with a cutoff frequency of 0 is defined as the null function
by convention here. This allows for a highpass with a cutoff of 0 to
be equal to identity, as defined in `julius.filters.HighPassFilters`.
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.
>>> lowpass = LowPassFilters([1/4])
>>> x = torch.randn(4, 12, 21, 1024)
>>> list(lowpass(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.cutoffs = list(cutoffs)
if min(self.cutoffs) < 0:
raise ValueError("Minimum cutoff must be larger than zero.")
if max(self.cutoffs) > 0.5:
raise ValueError("A cutoff above 0.5 does not make sense.")
self.stride = stride
self.pad = pad
self.zeros = zeros
self.half_size = int(zeros / min([c for c in self.cutoffs if c > 0]) / 2)
if fft is None:
fft = self.half_size > 32
self.fft = fft
window = torch.hann_window(2 * self.half_size + 1, periodic=False)
time = torch.arange(-self.half_size, self.half_size + 1)
filters = []
for cutoff in cutoffs:
if cutoff == 0:
filter_ = torch.zeros_like(time)
else:
filter_ = 2 * cutoff * window * sinc(2 * cutoff * math.pi * time)
# Normalize filter to have sum = 1, otherwise we will have a small leakage
# of the constant component in the input signal.
filter_ /= filter_.sum()
filters.append(filter_)
self.register_buffer("filters", torch.stack(filters)[:, None])
def forward(self, input):
shape = list(input.shape)
input = input.view(-1, 1, shape[-1])
if self.pad:
input = F.pad(input, (self.half_size, self.half_size), mode='replicate')
if self.fft:
out = fft_conv1d(input, self.filters, stride=self.stride)
else:
out = F.conv1d(input, self.filters, stride=self.stride)
shape.insert(0, len(self.cutoffs))
shape[-1] = out.shape[-1]
return out.permute(1, 0, 2).reshape(shape)
def __repr__(self):
return simple_repr(self)
class LowPassFilter(torch.nn.Module):
"""
Same as `LowPassFilters` but applies a single low pass filter.
Shape:
- Input: `[*, T]`
- Output: `[*, T']`, with `T'=T` if `pad` is True and `stride` is 1.
>>> lowpass = LowPassFilter(1/4, stride=2)
>>> x = torch.randn(4, 124)
>>> list(lowpass(x).shape)
[4, 62]
"""
def __init__(self, cutoff: float, stride: int = 1, pad: bool = True,
zeros: float = 8, fft: Optional[bool] = None):
super().__init__()
self._lowpasses = LowPassFilters([cutoff], stride, pad, zeros, fft)
@property
def cutoff(self):
return self._lowpasses.cutoffs[0]
@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):
return self._lowpasses(input)[0]
def __repr__(self):
return simple_repr(self)
def lowpass_filters(input: torch.Tensor, cutoffs: Sequence[float],
stride: int = 1, pad: bool = True,
zeros: float = 8, fft: Optional[bool] = None):
"""
Functional version of `LowPassFilters`, refer to this class for more information.
"""
return LowPassFilters(cutoffs, stride, pad, zeros, fft).to(input)(input)
def lowpass_filter(input: torch.Tensor, cutoff: float,
stride: int = 1, pad: bool = True,
zeros: float = 8, fft: Optional[bool] = None):
"""
Same as `lowpass_filters` but with a single cutoff frequency.
Output will not have a dimension inserted in the front.
"""
return lowpass_filters(input, [cutoff], stride, pad, zeros, fft)[0]
Functions
def lowpass_filter(input: torch.Tensor, cutoff: float, stride: int = 1, pad: bool = True, zeros: float = 8, fft: Optional[bool] = None)
-
Same as
lowpass_filters()
but with a single cutoff frequency. Output will not have a dimension inserted in the front.Expand source code Browse git
def lowpass_filter(input: torch.Tensor, cutoff: float, stride: int = 1, pad: bool = True, zeros: float = 8, fft: Optional[bool] = None): """ Same as `lowpass_filters` but with a single cutoff frequency. Output will not have a dimension inserted in the front. """ return lowpass_filters(input, [cutoff], stride, pad, zeros, fft)[0]
def lowpass_filters(input: torch.Tensor, cutoffs: Sequence[float], stride: int = 1, pad: bool = True, zeros: float = 8, fft: Optional[bool] = None)
-
Functional version of
LowPassFilters
, refer to this class for more information.Expand source code Browse git
def lowpass_filters(input: torch.Tensor, cutoffs: Sequence[float], stride: int = 1, pad: bool = True, zeros: float = 8, fft: Optional[bool] = None): """ Functional version of `LowPassFilters`, refer to this class for more information. """ return LowPassFilters(cutoffs, stride, pad, zeros, fft).to(input)(input)
Classes
class LowPassFilter (cutoff: float, stride: int = 1, pad: bool = True, zeros: float = 8, fft: Optional[bool] = None)
-
Same as
LowPassFilters
but applies a single low pass filter.Shape
- Input:
[*, T]
- Output:
[*, T']
, withT'=T
ifpad
is True andstride
is 1.
>>> lowpass = LowPassFilter(1/4, stride=2) >>> x = torch.randn(4, 124) >>> list(lowpass(x).shape) [4, 62]
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Expand source code Browse git
class LowPassFilter(torch.nn.Module): """ Same as `LowPassFilters` but applies a single low pass filter. Shape: - Input: `[*, T]` - Output: `[*, T']`, with `T'=T` if `pad` is True and `stride` is 1. >>> lowpass = LowPassFilter(1/4, stride=2) >>> x = torch.randn(4, 124) >>> list(lowpass(x).shape) [4, 62] """ def __init__(self, cutoff: float, stride: int = 1, pad: bool = True, zeros: float = 8, fft: Optional[bool] = None): super().__init__() self._lowpasses = LowPassFilters([cutoff], stride, pad, zeros, fft) @property def cutoff(self): return self._lowpasses.cutoffs[0] @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): return self._lowpasses(input)[0] def __repr__(self): return simple_repr(self)
Ancestors
- torch.nn.modules.module.Module
Class variables
var dump_patches : bool
var training : bool
Instance variables
var cutoff
-
Expand source code Browse git
@property def cutoff(self): return self._lowpasses.cutoffs[0]
var fft
-
Expand source code Browse git
@property def fft(self): return self._lowpasses.fft
var pad
-
Expand source code Browse git
@property def pad(self): return self._lowpasses.pad
var stride
-
Expand source code Browse git
@property def stride(self): return self._lowpasses.stride
var zeros
-
Expand source code Browse git
@property def zeros(self): return self._lowpasses.zeros
Methods
def forward(self, input) ‑> Callable[..., Any]
-
Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the :class:
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.Expand source code Browse git
def forward(self, input): return self._lowpasses(input)[0]
- Input:
class LowPassFilters (cutoffs: Sequence[float], stride: int = 1, pad: bool = True, zeros: float = 8, fft: Optional[bool] = None)
-
Bank of low pass filters. Note that a high pass or band pass filter can easily be implemented by substracting a same signal processed with low pass filters with different frequencies (see
SplitBands
for instance). This uses a windowed sinc filter, very similar to the one used injulius.resample
. However, because we do not change the sample rate here, this filter can be much more efficiently implemented using the FFT convolution fromjulius.fftconv
.Args
cutoffs
:list[float]
- list of cutoff frequencies, in [0, 0.5] expressed as
f/f_s
where f_s is the samplerate andf
is the cutoff frequency. The upper limit is 0.5, because a signal sampled atf_s
contains only frequencies underf_s / 2
. stride
:int
- how much to decimate the output. Keep in mind that decimation
of the output is only acceptable if the cutoff frequency is under
1/ (2 * stride)
of the original sampling rate. 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 lowpass 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
orNone
- 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.
Note
A lowpass with a cutoff frequency of 0 is defined as the null function by convention here. This allows for a highpass with a cutoff of 0 to be equal to identity, as defined in
HighPassFilters
.Shape
- Input:
[*, T]
- Output:
[F, *, T']
, withT'=T
ifpad
is True andstride
is 1, andF
is the numer of cutoff frequencies.
>>> lowpass = LowPassFilters([1/4]) >>> x = torch.randn(4, 12, 21, 1024) >>> list(lowpass(x).shape) [1, 4, 12, 21, 1024]
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Expand source code Browse git
class LowPassFilters(torch.nn.Module): """ Bank of low pass filters. Note that a high pass or band pass filter can easily be implemented by substracting a same signal processed with low pass filters with different frequencies (see `julius.bands.SplitBands` for instance). This uses a windowed sinc filter, very similar to the one used in `julius.resample`. However, because we do not change the sample rate here, this filter can be much more efficiently implemented using the FFT convolution from `julius.fftconv`. 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. Keep in mind that decimation of the output is only acceptable if the cutoff frequency is under `1/ (2 * stride)` of the original sampling rate. 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 lowpass 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. ..note:: A lowpass with a cutoff frequency of 0 is defined as the null function by convention here. This allows for a highpass with a cutoff of 0 to be equal to identity, as defined in `julius.filters.HighPassFilters`. 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. >>> lowpass = LowPassFilters([1/4]) >>> x = torch.randn(4, 12, 21, 1024) >>> list(lowpass(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.cutoffs = list(cutoffs) if min(self.cutoffs) < 0: raise ValueError("Minimum cutoff must be larger than zero.") if max(self.cutoffs) > 0.5: raise ValueError("A cutoff above 0.5 does not make sense.") self.stride = stride self.pad = pad self.zeros = zeros self.half_size = int(zeros / min([c for c in self.cutoffs if c > 0]) / 2) if fft is None: fft = self.half_size > 32 self.fft = fft window = torch.hann_window(2 * self.half_size + 1, periodic=False) time = torch.arange(-self.half_size, self.half_size + 1) filters = [] for cutoff in cutoffs: if cutoff == 0: filter_ = torch.zeros_like(time) else: filter_ = 2 * cutoff * window * sinc(2 * cutoff * math.pi * time) # Normalize filter to have sum = 1, otherwise we will have a small leakage # of the constant component in the input signal. filter_ /= filter_.sum() filters.append(filter_) self.register_buffer("filters", torch.stack(filters)[:, None]) def forward(self, input): shape = list(input.shape) input = input.view(-1, 1, shape[-1]) if self.pad: input = F.pad(input, (self.half_size, self.half_size), mode='replicate') if self.fft: out = fft_conv1d(input, self.filters, stride=self.stride) else: out = F.conv1d(input, self.filters, stride=self.stride) shape.insert(0, len(self.cutoffs)) shape[-1] = out.shape[-1] return out.permute(1, 0, 2).reshape(shape) def __repr__(self): return simple_repr(self)
Ancestors
- torch.nn.modules.module.Module
Class variables
var dump_patches : bool
var training : bool
Methods
def forward(self, input) ‑> Callable[..., Any]
-
Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the :class:
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.Expand source code Browse git
def forward(self, input): shape = list(input.shape) input = input.view(-1, 1, shape[-1]) if self.pad: input = F.pad(input, (self.half_size, self.half_size), mode='replicate') if self.fft: out = fft_conv1d(input, self.filters, stride=self.stride) else: out = F.conv1d(input, self.filters, stride=self.stride) shape.insert(0, len(self.cutoffs)) shape[-1] = out.shape[-1] return out.permute(1, 0, 2).reshape(shape)