Source Separation using Regularized NMF with MMSE Estimates under GMM Priors with Online Learning for The Uncertainties

TL;DR

This paper introduces a regularized NMF method guided by MMSE estimates under GMM priors, enhancing single-channel source separation by learning distortion directly from observed signals.

Contribution

It develops a novel regularized NMF framework incorporating MMSE estimates under GMM priors and online learning of distortion, improving source separation results.

Findings

Improved separation performance over traditional NMF.

Effective online learning of distortion from observed signals.

Enhanced denoising capabilities in source separation.

Abstract

We propose a new method to enforce priors on the solution of the nonnegative matrix factorization (NMF). The proposed algorithm can be used for denoising or single-channel source separation (SCSS) applications. The NMF solution is guided to follow the Minimum Mean Square Error (MMSE) estimates under Gaussian mixture prior models (GMM) for the source signal. In SCSS applications, the spectra of the observed mixed signal are decomposed as a weighted linear combination of trained basis vectors for each source using NMF. In this work, the NMF decomposition weight matrices are treated as a distorted image by a distortion operator, which is learned directly from the observed signals. The MMSE estimate of the weights matrix under GMM prior and log-normal distribution for the distortion is then found to improve the NMF decomposition results. The MMSE estimate is embedded within the optimization objective to form a novel regularized NMF cost function. The corresponding update rules for the new objectives are derived in this paper. Experimental results show that, the proposed regularized NMF algorithm improves the source separation performance compared with using NMF without prior or with other prior models.