mindspore.dataset.audio.transforms.BassBiquad

class mindspore.dataset.audio.transforms.BassBiquad(sample_rate, gain, central_freq=100.0, Q=0.707)

Design a bass tone-control effect, also known as two-pole low-shelf filter for audio waveform.

A low-shelf filter passes all frequencies, but increase or reduces frequencies below the shelf frequency by specified amount. The system function is:

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

Similar to SoX implementation.

Note

The dimension of the audio waveform to be processed needs to be (…, time).

Parameters

• sample_rate (int) – Sampling rate (in Hz), which can’t be zero.

• gain (float) – Desired gain at the boost (or attenuation) in dB.

• central_freq (float, optional) – Central frequency (in Hz). Default: 100.0.

• Q (float, optional) – Quality factor , in range of (0, 1]. Default: 0.707.

Raises

• TypeError – If sample_rate is not of type integer.

• ValueError – If sample_rate is 0.

• TypeError – If gain is not of type float.

• 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
>>>
>>> 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.BassBiquad(44100, 100.0)]
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms, input_columns=["audio"])