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Showing content from https://docs.nvidia.com/deeplearning/dali/user-guide/docs/operations/nvidia.dali.fn.mfcc.html below:

nvidia.dali.fn.mfcc — NVIDIA DALI

nvidia.dali.fn.mfcc(__input, /, *, axis=0, bytes_per_sample_hint=[0], dct_type=2, lifter=0.0, n_mfcc=20, normalize=False, preserve=False, device=None, name=None)#

Computes Mel Frequency Cepstral Coefficients (MFCC) from a mel spectrogram.

Supported backends

• ‘cpu’

• ‘gpu’

Parameters:

__input¶ (TensorList) – Input to the operator.

Keyword Arguments:

• axis¶ (int, optional, default = 0) –

  Axis over which the transform will be applied.

  If a value is not provided, the outer-most dimension will be used.

• bytes_per_sample_hint¶ (int or list of int, optional, default = [0]) –

  Output size hint, in bytes per sample.

  If specified, the operator’s outputs residing in GPU or page-locked host memory will be preallocated to accommodate a batch of samples of this size.

• dct_type¶ (int, optional, default = 2) –

  Discrete Cosine Transform type.

  The supported types are 1, 2, 3, 4. The formulas that are used to calculate the DCT are equivalent to those described in https://en.wikipedia.org/wiki/Discrete_cosine_transform (the numbers correspond to types listed in https://en.wikipedia.org/wiki/Discrete_cosine_transform#Formal_definition).

• lifter¶ (float, optional, default = 0.0) –

  Cepstral filtering coefficient, which is also known as the liftering coefficient.

  If the lifter coefficient is greater than 0, the MFCCs will be scaled based on the following formula:

  MFFC[i] = MFCC[i] * (1 + sin(pi * (i + 1) / lifter)) * (lifter / 2)
  

• n_mfcc¶ (int, optional, default = 20) – Number of MFCC coefficients.

• normalize¶ (bool, optional, default = False) –

  If set to True, the DCT uses an ortho-normal basis.

  Note

  Normalization is not supported when dct_type=1.

• preserve¶ (bool, optional, default = False) – Prevents the operator from being removed from the graph even if its outputs are not used.

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