InverseBarkScale

class torchaudio.prototype.transforms.InverseBarkScale(n_stft: int, n_barks: int = 128, sample_rate: int = 16000, f_min: float = 0.0, f_max: Optional[float] = None, max_iter: int = 100000, tolerance_loss: float = 1e-05, tolerance_change: float = 1e-08, sgdargs: Optional[dict] = None, 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.

Estimate a STFT in normal frequency domain from bark frequency domain.

It minimizes the euclidian norm between the input bark-spectrogram and the product between the estimated spectrogram and the filter banks using SGD.

Args:

n_stft (int): Number of bins in STFT. See n_fft in Spectrogram. n_barks (int, optional): Number of bark filterbanks. (Default: 128) sample_rate (int, optional): Sample rate of audio signal. (Default: 16000) f_min (float, optional): Minimum frequency. (Default: 0.) f_max (float or None, optional): Maximum frequency. (Default: sample_rate // 2) max_iter (int, optional): Maximum number of optimization iterations. (Default: 100000) tolerance_loss (float, optional): Value of loss to stop optimization at. (Default: 1e-5) tolerance_change (float, optional): Difference in losses to stop optimization at. (Default: 1e-8) sgdargs (dict or None, optional): Arguments for the SGD optimizer. (Default: None) bark_scale (str, optional): Scale to use: traunmuller, schroeder or wang. (Default: traunmuller)

Example

>>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
>>> mel_spectrogram_transform = transforms.BarkSpectrogram(sample_rate, n_fft=1024)
>>> mel_spectrogram = bark_spectrogram_transform(waveform)
>>> inverse_barkscale_transform = transforms.InverseBarkScale(n_stft=1024 // 2 + 1)
>>> spectrogram = inverse_barkscale_transform(mel_spectrogram)

forward(barkspec: Tensor) → Tensor

Parameters

barkspec (torch.Tensor) – A Bark frequency spectrogram of dimension (…, n_barks, time)

Returns

Linear scale spectrogram of size (…, freq, time)

Return type

torch.Tensor