mindspore.dataset.audio.LowpassBiquad
- class mindspore.dataset.audio.LowpassBiquad(sample_rate, cutoff_freq, Q=0.707)[source]
Design two-pole low-pass filter for audio waveform.
A low-pass filter passes frequencies lower than a selected cutoff frequency but attenuates frequencies higher than it. The system function is:
\[H(s) = \frac{1}{s^2 + \frac{s}{Q} + 1}\]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.
cutoff_freq (float) – Filter cutoff 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 cutoff_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, 10]) # 5 samples >>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"]) >>> transforms = [audio.LowpassBiquad(4000, 1500, 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 (10,) float64 >>> >>> # Use the transform in eager mode >>> waveform = np.random.random([10]) # 1 sample >>> output = audio.LowpassBiquad(4000, 1500, 0.7)(waveform) >>> print(output.shape, output.dtype) (10,) float64
- Tutorial Examples: