Add filter bank figures (#1891)

torchaudio/functional/functional.py

@@ -543,6 +543,13 @@ def melscale_fbanks(
 ) -> Tensor:
     r"""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.
+
+        .. image:: https://download.pytorch.org/torchaudio/doc-assets/mel_fbanks.png
+           :alt: Visualization of generated filter bank
+
     Args:
         n_freqs (int): Number of frequencies to highlight/apply
         f_min (float): Minimum frequency (Hz)
@@ -559,6 +566,7 @@ def melscale_fbanks(
         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), ...)``.
+
     """
 
     if norm is not None and norm != "slaney":
@@ -601,6 +609,13 @@ def linear_fbanks(
 ) -> Tensor:
     r"""Creates a linear triangular filterbank.
 
+    Note:
+        For the sake of the numerical compatibility with librosa, not all the coefficients
+        in the resulting filter bank has magnitude of 1.
+
+        .. image:: https://download.pytorch.org/torchaudio/doc-assets/lin_fbanks.png
+           :alt: Visualization of generated filter bank
+
     Args:
         n_freqs (int): Number of frequencies to highlight/apply
         f_min (float): Minimum frequency (Hz)

torchaudio/transforms.py

@@ -344,6 +344,10 @@ class MelScale(torch.nn.Module):
         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``)
+
+    See also:
+        :py:func:`torchaudio.functional.melscale_fbanks` - The function used to
+        generate the filter banks.
     """
     __constants__ = ['n_mels', 'sample_rate', 'f_min', 'f_max']
 
@@ -483,8 +487,10 @@ class InverseMelScale(torch.nn.Module):
 
 
 class MelSpectrogram(torch.nn.Module):
-    r"""Create MelSpectrogram for a raw audio signal. This is a composition of Spectrogram
-    and MelScale.
+    r"""Create MelSpectrogram for a raw audio signal.
+
+    This is a composition of :py:func:`torchaudio.transforms.Spectrogram` and
+    and :py:func:`torchaudio.transforms.MelScale`.
 
     Sources
         * https://gist.github.com/kastnerkyle/179d6e9a88202ab0a2fe
@@ -521,6 +527,10 @@ class MelSpectrogram(torch.nn.Module):
         >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True)
         >>> transform = transforms.MelSpectrogram(sample_rate)
         >>> mel_specgram = transform(waveform)  # (channel, n_mels, time)
+
+    See also:
+        :py:func:`torchaudio.functional.melscale_fbanks` - The function used to
+        generate the filter banks.
     """
     __constants__ = ['sample_rate', 'n_fft', 'win_length', 'hop_length', 'pad', 'n_mels', 'f_min']
 
@@ -599,6 +609,10 @@ class MFCC(torch.nn.Module):
         norm (str, optional): norm to use. (Default: ``'ortho'``)
         log_mels (bool, optional): whether to use log-mel spectrograms instead of db-scaled. (Default: ``False``)
         melkwargs (dict or None, optional): arguments for MelSpectrogram. (Default: ``None``)
+
+    See also:
+        :py:func:`torchaudio.functional.melscale_fbanks` - The function used to
+        generate the filter banks.
     """
     __constants__ = ['sample_rate', 'n_mfcc', 'dct_type', 'top_db', 'log_mels']
 
@@ -670,6 +684,11 @@ class LFCC(torch.nn.Module):
         norm (str, optional): norm to use. (Default: ``'ortho'``)
         log_lf (bool, optional): whether to use log-lf spectrograms instead of db-scaled. (Default: ``False``)
         speckwargs (dict or None, optional): arguments for Spectrogram. (Default: ``None``)
+
+
+    See also:
+        :py:func:`torchaudio.functional.linear_fbanks` - The function used to
+        generate the filter banks.
     """
     __constants__ = ['sample_rate', 'n_filter', 'n_lfcc', 'dct_type', 'top_db', 'log_lf']