A Tikhonov regularization method for spectrum decomposition in low latency audio source separation

We present the use of a Tikhonov regularization based method, as an alternative to the Non-negative Matrix Factorization (NMF) approach, for source separation in professional audio recordings. This method is a direct and computationally less expensive solution to the problem, which makes it interesting in low latency scenarios. The technique sacrifices the non-negativity constraint that characterizes NMF in exchange for a closed-form solution to the problem of spectrum factorization. We quantitatively evaluated it in terms of reconstruction and separation quality on a dataset of excerpts of professionally recorded songs with singing voice. Results show that the the proposed approach achieves similar quality to that of NMF.