mindspore.dataset.audio.LFCC

class mindspore.dataset.audio.LFCC(sample_rate=16000, n_filter=128, n_lfcc=40, f_min=0.0, f_max=None, dct_type=2, norm=NormMode.ORTHO, log_lf=False, speckwargs=None)

Create LFCC for a raw audio signal.

Note

The shape of the audio waveform to be processed needs to be <…, time>.

Parameters

• sample_rate (int, optional) – Sample rate of audio signal. Default: 16000.

• n_filter (int, optional) – Number of linear filters to apply. Default: 128.

• n_lfcc (int, optional) – Number of lfc coefficients to retain. Default: 40.

• f_min (float, optional) – Minimum frequency. Default: 0.0.

• f_max (float, optional) – Maximum frequency. Default: None, will be set to sample_rate // 2 .

• dct_type (int, optional) – Type of DCT to use. The value can only be 2. Default: 2.

• norm (NormMode, optional) – Norm to use. Default: NormMode.ORTHO.

• log_lf (bool, optional) – Whether to use log-lf spectrograms instead of db-scaled. Default: False.

• speckwargs (dict, optional) –

  Arguments for mindspore.dataset.audio.Spectrogram. Default: None, the default setting is a dict including

  • ’n_fft’: 400

  • ’win_length’: n_fft

  • ’hop_length’: win_length // 2

  • ’pad’: 0

  • ’window’: WindowType.HANN

  • ’power’: 2.0

  • ’normalized’: False

  • ’center’: True

  • ’pad_mode’: BorderType.REFLECT

  • ’onesided’: True

Raises

• TypeError – If sample_rate is not of type int.

• TypeError – If n_filter is not of type int.

• TypeError – If n_lfcc is not of type int.

• TypeError – If norm is not of type mindspore.dataset.audio.NormMode .

• TypeError – If log_lf is not of type bool.

• TypeError – If speckwargs is not of type dict.

• ValueError – If sample_rate is 0.

• ValueError – If n_lfcc is less than 0.

• ValueError – If f_min is greater than f_max .

• ValueError – If f_min is greater than sample_rate // 2 when f_max is set to None.

• ValueError – If dct_type is not 2.

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, 300])
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"])
>>> transforms = [audio.LFCC()]
>>> 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, 40, 2) float32
>>>
>>> # Use the transform in eager mode
>>> waveform = np.random.random([10, 300])  # 1 sample
>>> output = audio.LFCC()(waveform)
>>> print(output.shape, output.dtype)
(10, 40, 2) float32

Tutorial Examples:

• Illustration of audio transforms