Module julius.filters

FIR windowed sinc highpass and bandpass filters. Those are convenience wrappers around the filters defined in julius.lowpass.

Functions

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.

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.

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.

Classes

class BandPassFilter (cutoff_low: float, cutoff_high: float, stride: int = 1, pad: bool = True, zeros: float = 8, fft: Optional[bool] = None)

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 SplitBands.

>>> bandpass = BandPassFilter(1/4, 1/3)
>>> x = torch.randn(4, 12, 21, 1024)
>>> list(bandpass(x).shape)
[4, 12, 21, 1024]

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Expand source code Browse git 
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)

Ancestors

  • torch.nn.modules.module.Module

Class variables

var call_super_init : bool
var dump_patches : bool
var training : bool

Instance variables

prop cutoff_high
Expand source code 
@property
def cutoff_high(self):
    return self._lowpasses.cutoffs[1]
prop cutoff_low
Expand source code 
@property
def cutoff_low(self):
    return self._lowpasses.cutoffs[0]
prop fft
Expand source code 
@property
def fft(self):
    return self._lowpasses.fft
prop pad
Expand source code 
@property
def pad(self):
    return self._lowpasses.pad
prop stride
Expand source code 
@property
def stride(self):
    return self._lowpasses.stride
prop zeros
Expand source code 
@property
def zeros(self):
    return self._lowpasses.zeros

Methods

def forward(self, input) ‑> Callable[..., Any]

Define 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.

class HighPassFilter (cutoff: float, stride: int = 1, pad: bool = True, zeros: float = 8, fft: Optional[bool] = None)

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]

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Expand source code Browse git 
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)

Ancestors

  • torch.nn.modules.module.Module

Class variables

var call_super_init : bool
var dump_patches : bool
var training : bool

Instance variables

prop cutoff
Expand source code 
@property
def cutoff(self):
    return self._highpasses.cutoffs[0]
prop fft
Expand source code 
@property
def fft(self):
    return self._highpasses.fft
prop pad
Expand source code 
@property
def pad(self):
    return self._highpasses.pad
prop stride
Expand source code 
@property
def stride(self):
    return self._highpasses.stride
prop zeros
Expand source code 
@property
def zeros(self):
    return self._highpasses.zeros

Methods

def forward(self, input) ‑> Callable[..., Any]

Define 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.

class HighPassFilters (cutoffs: Sequence[float], stride: int = 1, pad: bool = True, zeros: float = 8, fft: Optional[bool] = None)

Bank of high pass filters. See 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]

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Expand source code Browse git 
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)

Ancestors

  • torch.nn.modules.module.Module

Class variables

var call_super_init : bool
var dump_patches : bool
var training : bool

Instance variables

prop cutoffs
Expand source code 
@property
def cutoffs(self):
    return self._lowpasses.cutoffs
prop fft
Expand source code 
@property
def fft(self):
    return self._lowpasses.fft
prop pad
Expand source code 
@property
def pad(self):
    return self._lowpasses.pad
prop stride
Expand source code 
@property
def stride(self):
    return self._lowpasses.stride
prop zeros
Expand source code 
@property
def zeros(self):
    return self._lowpasses.zeros

Methods

def forward(self, input) ‑> Callable[..., Any]

Define 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.