speechbrain.processing.NMF module¶
Non-negative matrix factorization
- Authors
Cem Subakan
Summary¶
Functions:
This function separates the mixture signals, given NMF template matrices. |
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This function reconstructs the separated spectra into waveforms. |
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Returns the phase of a complex spectrogram. |
Reference¶
- speechbrain.processing.NMF.spectral_phase(stft, power=2, log=False)[source]¶
Returns the phase of a complex spectrogram.
- Parameters
stft (torch.Tensor) – A tensor, output from the stft function.
Example
>>> BS, nfft, T = 10, 20, 300 >>> X_stft = torch.randn(BS, nfft//2 + 1, T, 2) >>> phase_mix = spectral_phase(X_stft)
- speechbrain.processing.NMF.NMF_separate_spectra(Whats, Xmix)[source]¶
This function separates the mixture signals, given NMF template matrices.
- Parameters
Whats (list) – This list contains the list [W1, W2], where W1 W2 are respectively the NMF template matrices that correspond to source1 and source2. W1, W2 are of size [nfft/2 + 1, K], where nfft is the fft size for STFT, and K is the number of vectors (templates) in W.
Xmix (torch.tensor) – This is the magnitude spectra for the mixtures. The size is [BS x T x nfft//2 + 1] where, BS = batch size, nfft = fft size, T = number of time steps in the spectra.
Outputs –
------- –
X1hat (Separated spectrum for source1) – Size = [BS x (nfft/2 +1) x T] where, BS = batch size, nfft = fft size, T = number of time steps in the spectra.
X2hat (Separated Spectrum for source2) – The size definitions are the same as above.
Example
>>> BS, nfft, T = 4, 20, 400 >>> K1, K2 = 10, 10 >>> W1hat = torch.randn(nfft//2 + 1, K1) >>> W2hat = torch.randn(nfft//2 + 1, K2) >>> Whats = [W1hat, W2hat] >>> Xmix = torch.randn(BS, T, nfft//2 + 1) >>> X1hat, X2hat = NMF_separate_spectra(Whats, Xmix)
- speechbrain.processing.NMF.reconstruct_results(X1hat, X2hat, X_stft, sample_rate, win_length, hop_length)[source]¶
This function reconstructs the separated spectra into waveforms.
- Parameters
Xhat1 (torch.tensor) – The separated spectrum for source 1 of size [BS, nfft/2 + 1, T], where, BS = batch size, nfft = fft size, T = length of the spectra.
Xhat2 (torch.tensor) – The separated spectrum for source 2 of size [BS, nfft/2 + 1, T]. The size definitions are the same as Xhat1.
X_stft (torch.tensor) – This is the magnitude spectra for the mixtures. The size is [BS x nfft//2 + 1 x T x 2] where, BS = batch size, nfft = fft size, T = number of time steps in the spectra. The last dimension is to represent complex numbers.
sample_rate (int) – The sampling rate (in Hz) in which we would like to save the results.
win_length (int) – The length of stft windows (in ms).
hop_length (int) – The length with which we shift the STFT windows (in ms).
- Returns
x1hats (list) – List of waveforms for source 1.
x2hats (list) – List of waveforms for source 2.
Example
>>> BS, nfft, T = 10, 512, 16000 >>> sample_rate, win_length, hop_length = 16000, 25, 10 >>> X1hat = torch.randn(BS, nfft//2 + 1, T) >>> X2hat = torch.randn(BS, nfft//2 + 1, T) >>> X_stft = torch.randn(BS, nfft//2 + 1, T, 2) >>> x1hats, x2hats = reconstruct_results(X1hat, X2hat, X_stft, sample_rate, win_length, hop_length)