Enhancing audio source separability using spectro-temporal regularization with NMF

We propose a spectro-temporal regularization approach for NMF that accounts for a source’s spectral variability over time. The regularization terms allow NMF to adapt the spectral basis matrices optimally to reduce mismatch between the spectral characteristics of sources observed during training and encountered during separation. We first tested our algorithm on a simulated source separation task. Preliminary results show significant improvement of SAR, SDR, and SIR values over some current NMF methods. We also tested our algorithm on a speech enhancement task and were able to show a modest improvement of the PESQ scores of the recovered speech.