mindspore.dataset.audio.BandrejectBiquad

class mindspore.dataset.audio.BandrejectBiquad(sample_rate, central_freq, Q=0.707)[source]

Design two-pole Butterworth band-reject filter for audio waveform.

The frequency response of the Butterworth filter is maximally flat (i.e. has no ripples) in the passband and rolls off towards zero in the stopband.

The system function of Butterworth band-reject filter is:

\[H(s) = \frac{s^2 + 1}{s^2 + \frac{s}{Q} + 1}\]

Similar to SoX implementation.

Note

The shape of the audio waveform to be processed needs to be <…, time>.

Parameters

sample_rate (int) – Sampling rate (in Hz), which can’t be zero.
central_freq (float) – Central frequency (in Hz).
Q (float, optional) – Quality factor , in range of (0, 1]. Default: 0.707.

Raises

TypeError – If sample_rate is not of type int.
ValueError – If sample_rate is 0.
TypeError – If central_freq is not of type float.
TypeError – If Q is not of type float.
ValueError – If Q is not in range of (0, 1].
RuntimeError – If input tensor is not in shape of <…, time>.

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.BandrejectBiquad(44100, 200.0)]
>>> 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.BandrejectBiquad(44100, 200.0)(waveform)
>>> print(output.shape, output.dtype)
(16,) float64

Tutorial Examples:

Illustration of audio transforms