art.preprocessing.audio

This module contains audio preprocessing tools.

L-Filter

class art.preprocessing.audio.LFilter(numerator_coef: ndarray = array([1.]), denominator_coef: ndarray = array([1.]), axis: int = -1, initial_cond: ndarray | None = None, clip_values: CLIP_VALUES_TYPE | None = None, apply_fit: bool = False, apply_predict: bool = True, verbose: bool = False)

This module implements the filter function for audio signals. It provides with an infinite impulse response (IIR) or finite impulse response (FIR) filter. This implementation is a wrapper around the scipy.signal.lfilter function in the scipy package.

__call__(x: ndarray, y: ndarray | None = None) → Tuple[ndarray, ndarray | None]

Apply filter to sample x.

Parameters:

  • x (ndarray) – Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch could have different lengths. A possible example of x could be: x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])]).

  • y – Labels of the sample x. This function does not affect them in any way.

Returns:

Similar samples.

__init__(numerator_coef: ndarray = array([1.]), denominator_coef: ndarray = array([1.]), axis: int = -1, initial_cond: ndarray | None = None, clip_values: CLIP_VALUES_TYPE | None = None, apply_fit: bool = False, apply_predict: bool = True, verbose: bool = False)

Create an instance of LFilter.

Parameters:

  • numerator_coef (ndarray) – The numerator coefficient vector in a 1-D sequence.

  • denominator_coef (ndarray) – The denominator coefficient vector in a 1-D sequence. By simply setting the array of denominator coefficients to np.array([1.0]), this preprocessor can be used to apply a FIR filter.

  • axis (int) – The axis of the input data array along which to apply the linear filter. The filter is applied to each subarray along this axis.

  • initial_cond – Initial conditions for the filter delays.

  • clip_values – Tuple of the form (min, max) representing the minimum and maximum values allowed for features.

  • apply_fit (bool) – True if applied during fitting/training.

  • apply_predict (bool) – True if applied during predicting.

  • verbose (bool) – Show progress bars.

estimate_gradient(x: ndarray, grad: ndarray) → ndarray

Provide an estimate of the gradients of the defence for the backward pass.

Return type:

ndarray

Parameters:

  • x (ndarray) – Input data for which the gradient is estimated. First dimension is the batch size.

  • grad (ndarray) – Gradient value so far.

Returns:

The gradient (estimate) of the defence.

LFilter - PyTorch

class art.preprocessing.audio.LFilterPyTorch(numerator_coef: ndarray = array([1.]), denominator_coef: ndarray = array([1.]), clip_values: CLIP_VALUES_TYPE | None = None, apply_fit: bool = False, apply_predict: bool = True, verbose: bool = False, device_type: str = 'gpu')

This module implements the filter function for audio signals in PyTorch. It provides with an infinite impulse response (IIR) or finite impulse response (FIR) filter. This implementation is a wrapper around the torchaudio.functional.lfilter function in the torchaudio package.

__call__(x: ndarray, y: ndarray | None = None) → Tuple[ndarray, ndarray | None]

Apply filter to sample x.

Parameters:

  • x (ndarray) – Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch could have different lengths. A possible example of x could be: x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])]).

  • y – Labels of the sample x. This function does not affect them in any way.

Returns:

Similar samples.

__init__(numerator_coef: ndarray = array([1.]), denominator_coef: ndarray = array([1.]), clip_values: CLIP_VALUES_TYPE | None = None, apply_fit: bool = False, apply_predict: bool = True, verbose: bool = False, device_type: str = 'gpu') → None

Create an instance of LFilterPyTorch.

Parameters:

  • numerator_coef (ndarray) – The numerator coefficient vector in a 1-D sequence.

  • denominator_coef (ndarray) – The denominator coefficient vector in a 1-D sequence. By simply setting the array of denominator coefficients to np.array([1.0]), this preprocessor can be used to apply a FIR filter.

  • clip_values – Tuple of the form (min, max) representing the minimum and maximum values allowed for features.

  • apply_fit (bool) – True if applied during fitting/training.

  • apply_predict (bool) – True if applied during predicting.

  • verbose (bool) – Show progress bars.

  • device_type (str) – Type of device on which the classifier is run, either gpu or cpu.

forward(x: torch.Tensor, y: torch.Tensor | None = None) → Tuple[torch.Tensor, torch.Tensor | None]

Apply filter to a single sample x.

Parameters:

  • x – A single audio sample.

  • y – Label of the sample x. This function does not affect them in any way.

Returns:

Similar sample.