mindspore.dataset.audio.SlidingWindowCmn

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class mindspore.dataset.audio.SlidingWindowCmn(cmn_window=600, min_cmn_window=100, center=False, norm_vars=False)[source]

Apply sliding-window cepstral mean (and optionally variance) normalization per utterance.

Parameters

  • cmn_window (int, optional) – Window in frames for running average CMN computation. Default: 600.

  • min_cmn_window (int, optional) – Minimum CMN window used at start of decoding (adds latency only at start). Only applicable if center is False, ignored if center is True. Default: 100.

  • center (bool, optional) – If True, use a window centered on the current frame. If False, window is to the left. Default: False.

  • norm_vars (bool, optional) – If True, normalize variance to one. Default: False.

Raises

  • TypeError – If cmn_window is not of type int.

  • ValueError – If cmn_window is a negative number.

  • TypeError – If min_cmn_window is not of type int.

  • ValueError – If min_cmn_window is a negative number.

  • TypeError – If center is not of type bool.

  • TypeError – If norm_vars is not of type bool.

Supported Platforms:

CPU

Examples

>>> import numpy as np
>>> import mindspore.dataset as ds
>>> import mindspore.dataset.audio as audio
>>>
>>> waveform = np.array([[[1, 2, 3], [4, 5, 6]]], dtype=np.float64)
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data=waveform, column_names=["audio"])
>>> transforms = [audio.SlidingWindowCmn()]
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms, input_columns=["audio"])
Tutorial Examples: