ChromaScale

class torchaudio.prototype.transforms.ChromaScale(sample_rate: int, n_freqs: int, *, n_chroma: int = 12, tuning: float = 0.0, ctroct: float = 5.0, octwidth: Optional[float] = 2.0, norm: int = 2, base_c: bool = True)

Converts spectrogram to chromagram.

Parameters:

• sample_rate (int) – Sample rate of audio signal.

• n_freqs (int) – Number of frequency bins in STFT. See n_fft in Spectrogram.

• n_chroma (int, optional) – Number of chroma. (Default: 12)

• tuning (float, optional) – Tuning deviation from A440 in fractions of a chroma bin. (Default: 0.0)

• ctroct (float, optional) – Center of Gaussian dominance window to weight filters by, in octaves. (Default: 5.0)

• octwidth (float or None, optional) – Width of Gaussian dominance window to weight filters by, in octaves. If None, then disable weighting altogether. (Default: 2.0)

• norm (int, optional) – order of norm to normalize filter bank by. (Default: 2)

• base_c (bool, optional) – If True, then start filter bank at C. Otherwise, start at A. (Default: True)

Example

>>> waveform, sample_rate = torchaudio.load("test.wav", normalize=True)
>>> spectrogram_transform = transforms.Spectrogram(n_fft=1024)
>>> spectrogram = spectrogram_transform(waveform)
>>> chroma_transform = transforms.ChromaScale(sample_rate=sample_rate, n_freqs=1024 // 2 + 1)
>>> chroma_spectrogram = chroma_transform(spectrogram)

See also

torchaudio.prototype.functional.chroma_filterbank() — function used to generate the filter bank.

forward(x: Tensor) → Tensor

Parameters:

specgram (torch.Tensor) – Spectrogram of dimension (…, n_freqs, time).

Returns:

Chroma spectrogram of size (…, n_chroma, time).

Return type:

torch.Tensor