mindspore.dataset.audio.Biquad
- class mindspore.dataset.audio.Biquad(b0, b1, b2, a0, a1, a2)[source]
Perform a biquad filter of input audio. Mathematical fomulas refer to: Digital_biquad_filter .
- Parameters
b0 (float) – Numerator coefficient of current input, x[n].
b1 (float) – Numerator coefficient of input one time step ago x[n-1].
b2 (float) – Numerator coefficient of input two time steps ago x[n-2].
a0 (float) – Denominator coefficient of current output y[n], the value can't be 0, typically 1.
a1 (float) – Denominator coefficient of current output y[n-1].
a2 (float) – Denominator coefficient of current output y[n-2].
- Raises
- Supported Platforms:
CPU
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.Biquad(0.01, 0.02, 0.13, 1, 0.12, 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.Biquad(0.01, 0.02, 0.13, 1, 0.12, 0.3)(waveform) >>> print(output.shape, output.dtype) (16,) float64
- Tutorial Examples: