Gaussian Model Based Multichannel Separation

FastMVAE: A Fast Optimization Algorithm for the Multichannel Variational Autoencoder Method

“…Note that a particular case where T j = 1 and b j,t (f ) = 1 for all j is equivalent to assuming the norm r j (n) = f |s j (f, n)| 2 follows a complex Gaussian distribution with time-varying variance h j (n). This is analogous to the assumption in IVA that the magnitudes of the STFT coefficients in all frequency bands originating from the same source tend to vary coherently over time [32].…”

“…If there is a large number of utterances of a sufficiently wide variety of speakers in the training dataset, the trained model is expected to have an ability to express spectrograms of unseen speakers. When a test mixture contains unseen speakers, (31) can be interpreted as how similar speaker j is to the speakers in the training set, whereas (32) indicates the speaker in the training set most similar to speaker j. A test set was created by randomly mixing two different speakers selected from the WSJ0 folders si_dt_05 and si_et_05, where the number of speakers was 18.…”