BarkSpectrogram

class torchaudio.prototype.transforms.BarkSpectrogram(sample_rate: int = 16000, n_fft: int = 400, win_length: ~typing.Optional[int] = None, hop_length: ~typing.Optional[int] = None, f_min: float = 0.0, f_max: ~typing.Optional[float] = None, pad: int = 0, n_barks: int = 128, window_fn: ~typing.Callable[[...], ~torch.Tensor] = <built-in method hann_window of type object>, power: float = 2.0, normalized: bool = False, wkwargs: ~typing.Optional[dict] = None, center: bool = True, pad_mode: str = 'reflect', bark_scale: str = 'traunmuller')

DEPRECATED

Warning

This class is deprecated from version 2.8. It will be removed in the 2.9 release. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. Please see https://github.com/pytorch/audio/issues/3902 for more information.

Create BarkSpectrogram for a raw audio signal.

This is a composition of torchaudio.transforms.Spectrogram() and and torchaudio.transforms.BarkScale().

Sources

• https://www.fon.hum.uva.nl/praat/manual/BarkSpectrogram.html

• Traunmüller, Hartmut. “Analytical Expressions for the Tonotopic Sensory Scale.” Journal of the Acoustical

• Society of America. Vol. 88, Issue 1, 1990, pp. 97–100.

• https://ccrma.stanford.edu/courses/120-fall-2003/lecture-5.html

Args:

sample_rate (int, optional): Sample rate of audio signal. (Default: 16000) n_fft (int, optional): Size of FFT, creates n_fft // 2 + 1 bins. (Default: 400) win_length (int or None, optional): Window size. (Default: n_fft) hop_length (int or None, optional): Length of hop between STFT windows. (Default: win_length // 2) f_min (float, optional): Minimum frequency. (Default: 0.) f_max (float or None, optional): Maximum frequency. (Default: None) pad (int, optional): Two sided padding of signal. (Default: 0) n_mels (int, optional): Number of mel filterbanks. (Default: 128) window_fn (Callable[…, torch.Tensor], optional): A function to create a window tensor

that is applied/multiplied to each frame/window. (Default: torch.hann_window)

power (float, optional): Exponent for the magnitude spectrogram,

(must be > 0) e.g., 1 for energy, 2 for power, etc. (Default: 2)

normalized (bool, optional): Whether to normalize by magnitude after stft. (Default: False) wkwargs (Dict[…, …] or None, optional): Arguments for window function. (Default: None) center (bool, optional): whether to pad waveform on both sides so

that the \(t\)-th frame is centered at time \(t \times \text{hop\_length}\). (Default: True)

pad_mode (string, optional): controls the padding method used when

center is True. (Default: "reflect")

bark_scale (str, optional): Scale to use: traunmuller, schroeder or wang. (Default: traunmuller)

Example

>>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
>>> transform = transforms.BarkSpectrogram(sample_rate)
>>> bark_specgram = transform(waveform)  # (channel, n_barks, time)

See also:

torchaudio.functional.melscale_fbanks() - The function used to generate the filter banks.

forward(waveform: Tensor) → Tensor

Parameters

waveform (torch.Tensor) – torch.Tensor of audio of dimension (…, time).

Returns

Bark frequency spectrogram of size (…, n_barks, time).

Return type

torch.Tensor