mindspore.dataset.audio.Filtfilt
- class mindspore.dataset.audio.Filtfilt(a_coeffs, b_coeffs, clamp=True)[source]
Apply an IIR filter forward and backward to a waveform.
- Parameters
a_coeffs (Sequence[float]) – Denominator coefficients of difference equation of dimension. Lower delays coefficients are first, e.g. [a0, a1, a2, …]. Must be same size as b_coeffs (pad with 0's as necessary).
b_coeffs (Sequence[float]) – Numerator coefficients of difference equation of dimension. Lower delays coefficients are first, e.g. [b0, b1, b2, …]. Must be same size as a_coeffs (pad with 0's as necessary).
clamp (bool, optional) – If
True
, clamp the output signal to be in the range [-1, 1]. Default:True
.
- Raises
TypeError – If a_coeffs is not of type Sequence[float].
TypeError – If b_coeffs is not of type Sequence[float].
ValueError – If a_coeffs and b_coeffs are of different sizes.
TypeError – If clamp is not of type bool.
RuntimeError – If shape of the input audio is not <…, time>.
Examples
>>> import numpy as np >>> import mindspore.dataset as ds >>> import mindspore.dataset.audio as audio >>> >>> # Use the transform in dataset pipeline mode >>> waveform = np.random.random([5, 16]) # 5 samples >>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"]) >>> transforms = [audio.Filtfilt(a_coeffs=[0.1, 0.2, 0.3], b_coeffs=[0.1, 0.2, 0.3])] >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms, input_columns=["audio"]) >>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True): ... print(item["audio"].shape, item["audio"].dtype) ... break (16,) float64 >>> >>> # Use the transform in eager mode >>> waveform = np.random.random([16]) # 1 sample >>> output = audio.Filtfilt(a_coeffs=[0.1, 0.2, 0.3], b_coeffs=[0.1, 0.2, 0.3])(waveform) >>> print(output.shape, output.dtype) (16,) float64
- Tutorial Examples: