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首页> 外文会议>2016 IEEE International Workshop on Acoustic Signal Enhancement >Mask estimate through Itakura-Saito nonnegative RPCA for speech enhancement
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Mask estimate through Itakura-Saito nonnegative RPCA for speech enhancement

机译：通过Itakura-Saito非负RPCA进行掩码估计以增强语音

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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.
机译：掩模估计被认为是使用计算听觉场景分析方法来增强被噪声污染的语音的主要目标。本文提出了扩展的鲁棒主成分分析（RPCA）方法，分别称为NRPCA和ISNRPCA，以有效地估计掩码。通过NRPCA或ISNRPCA将感知动机的耳蜗图分解为稀疏分量和低秩分量，分别对应于语音和噪声。与经典的RPCA不同，NRPCA施加非负约束来规范分解后的组件。此外，ISNRPCA使用感知上有意义的Itakura-Saito度量作为其优化目标函数。我们使用乘法器的交替方向方法来解决相应的优化问题。 NRPCA和ISNRPCA完全不受监督，不需要预先训练语音或噪声模型。实验结果表明，NRPCA和ISNRPCA在语音增强方面显示出令人鼓舞的结果。关于现有技术的基准，所提出的方法在抑制噪声方面取得了更好的性能，并证明了至少可比的清晰度和整体质量。

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