mindspore.dataset.audio.BassBiquad

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

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 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.

  • 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
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.BassBiquad(44100, 100.0)]
>>> 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.BassBiquad(44100, 200.0)(waveform)
>>> print(output.shape, output.dtype)
(16,) float64
Tutorial Examples: