mindspore.dataset.audio.AllpassBiquad

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

Design two-pole all-pass filter with central frequency and bandwidth for audio waveform.

An all-pass filter changes the audio's frequency to phase relationship without changing its frequency to amplitude relationship. The system function is:

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

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

Tutorial Examples:

Illustration of audio transforms