Music signal separation based on Bayesian spectral amplitude estimator with automatic target prior adaptation
Yuki Murota, Daichi Kitamura, Shunsuke Nakai, Hiroshi Saruwatari, Satoshi Nakamura, Yu Takahashi, Kazunobu Kondo · 2014
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.