Superresolution-based stereo signal separation via supervised nonnegative matrix factorization

In this paper, we address a stereo signal separation problem and propose a new method utilizing both directional clustering and superresolution-based supervised nonnegative matrix factorization (NMF) via spectrogram extrapolation using supervised bases. In previous studies, a hybrid method concatenating supervised NMF after directional clustering was proposed as for multichannel signal separation. However, this hybrid method has a problem that the extracted signal suffers from considerable spectral distortion because directional clustering yields spectral chasms. To solve this problem, we propose a new supervised NMF algorithm that regards the spectral chasms as unseen observations and reconstructs the target source components via spectrogram extrapolation using supervised bases. Our experimental results show that the proposed method outperforms several conventional methods and that the distortion of the extracted signal can be mitigated by superresolution efficacy.