torchaudio.functional.melscale_fbanks

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

Create a frequency bin conversion matrix.

Note

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

Parameters:

• 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)

Returns:

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:

Tensor

Tutorials using melscale_fbanks:

Audio Feature Extractions

Audio Feature Extractions