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')

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.

Parameters:

• 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