mindspore.dataset.audio.EqualizerBiquad

class mindspore.dataset.audio.EqualizerBiquad(sample_rate, center_freq, gain, Q=0.707)[source]

Design biquad equalizer filter and perform filtering.

Similar to SoX implementation.

Parameters

sample_rate (int) – Sampling rate of the waveform, e.g. 44100 (Hz), the value can't be 0.
center_freq (float) – Central frequency (in Hz).
gain (float) – Desired gain at the boost (or attenuation) in dB.
Q (float, optional) – https://en.wikipedia.org/wiki/Q_factor, range: (0, 1]. Default: 0.707.

Raises

TypeError – If sample_rate is not of type int.
ValueError – If sample_rate is 0.
TypeError – If center_freq is not of type float.
TypeError – If gain is not of type float.
TypeError – If Q is not of type float.
ValueError – If Q is not in range of (0, 1].

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.EqualizerBiquad(44100, 1500, 5.5, 0.7)]
>>> 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.EqualizerBiquad(44100, 1500, 5.5, 0.7)(waveform)
>>> print(output.shape, output.dtype)
(16,) float64

Tutorial Examples:

Illustration of audio transforms