Music signal separation based on Bayesian spectral amplitude estimator with automatic target prior adaptation

In this paper, we propose a new approach for addressing music signal separation based on the generalized Bayesian estimator with automatic prior adaptation. This method consists of three parts, namely, the generalized MMSE-STSA estimator with a flexible target signal prior, the NMF-based dynamic interference spectrogram estimator, and closed-form parameter estimation for the statistical model of the target signal based on higher-order statistics. The statistical model parameter of the hidden target signal can be detected automatically for optimal Bayesian estimation with online target-signal prior adaptation. Our experimental evaluation can show the efficacy of the proposed method.