Mask estimate through Itakura-Saito nonnegative RPCA for speech enhancement
Gang Min, Xiongwei Zhang, Xia Zou, Meng Sun · 2016
Mask estimate is regarded as the main goal for using the computational auditory scene analysis method to enhance speech contaminated by noises. This paper presents extended robust principal component analysis (RPCA) methods, referred to as NRPCA and ISNRPCA, to estimate mask effectively. The perceptually motivated cochleagram is decomposed into sparse and low-rank components via NRPCA or ISNRPCA, which correspond to speech and noises, respectively. Different from the classical RPCA, NRPCA imposes nonnegative constraints to regularize the decomposed components. Furthermore, ISNRPCA uses the perceptually meaningful Itakura-Saito measure as its optimization objective function. We use the alternating direction method of multipliers to solve the corresponding optimization problem. NRPCA and ISNRPCA are totally unsupervised, neither speech nor noise model needs to be trained beforehand. Experimental results demonstrate that NRPCA and ISNRPCA show promising results for speech enhancement. With respect to state of the art baselines, the proposed methods achieve better performance on noises suppression and demonstrate at least comparable intelligibility and overall-quality.