mindspore.dataset.audio.BandBiquad

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

Design two-pole band-pass filter for audio waveform.

The frequency response drops logarithmically around the center frequency. The bandwidth gives the slope of the drop. The frequencies at band edge will be half of their original amplitudes.

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.
noise (bool, optional) – If True, uses the alternate mode for un-pitched audio (e.g. percussion). If False, uses mode oriented to pitched audio, i.e. voice, singing, or instrumental music. Default: False.

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].
TypeError – If noise is not of type bool.
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
>>>
>>> waveform = np.array([[2.716064453125e-03, 6.34765625e-03], [9.246826171875e-03, 1.0894775390625e-02]])
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"])
>>> transforms = [audio.BandBiquad(44100, 200.0)]
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms, input_columns=["audio"])

Tutorial Examples:

Illustration of audio transforms