torchaudio.functional.melscale_fbanks(n_freqs: int, f_min: float, f_max: float, n_mels: int, sample_rate: int, norm: Optional[str] = None, mel_scale: str = 'htk') Tensor[source]

Create a frequency bin conversion matrix.

This feature supports the following devices: CPU This API supports the following properties: TorchScript


For the sake of the numerical compatibility with librosa, not all the coefficients in the resulting filter bank has magnitude of 1.

Visualization of generated filter bank
  • n_freqs (int) – Number of frequencies to highlight/apply

  • f_min (float) – Minimum frequency (Hz)

  • f_max (float) – Maximum frequency (Hz)

  • n_mels (int) – Number of mel filterbanks

  • sample_rate (int) – Sample rate of the audio waveform

  • norm (str or None, optional) – If “slaney”, divide the triangular mel weights by the width of the mel band (area normalization). (Default: None)

  • mel_scale (str, optional) – Scale to use: htk or slaney. (Default: htk)


Triangular filter banks (fb matrix) of size (n_freqs, n_mels) meaning number of frequencies to highlight/apply to x the number of filterbanks. Each column is a filterbank so that assuming there is a matrix A of size (…, n_freqs), the applied result would be A @ melscale_fbanks(A.size(-1), ...).

Return type:


Tutorials using melscale_fbanks:
Audio Feature Extractions

Audio Feature Extractions

Audio Feature Extractions


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