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中文摘要：
提出一种基于非对称代价函数的稀疏卷积非负矩阵分解方法。该方法利用板仓-斋藤距离作为目标代价函数来衡量目标矩阵与重建矩阵的差异，使得较小的矩阵元素具有较小的重建误差，并且该代价函数具有尺度不变性的特点。为了考察其在弱语音成分重建方面的优势，将本文提出的算法应用于耳语音谱分解及重建实验。实验结果表明，与基于欧氏距离和基于 Kullback-Leibler（K-L）散度的卷积非负矩阵分解算法相比，本文算法对于弱语音成分具有更好的重构效果，重建后的语音信号具有较大的可懂度。
英文摘要：
A sparse convolutive non-negative matrix factorization method is proposed based on asymmetric cost function. The method utilizes the Itakura-Saito distance as the objective cost function to measure the error between a target matrix and its reconstruction version,making the smaller matrix element have a smaller reconstruction error,and the cost function has the property of scale invariant.In order to evaluate its advantage in the aspect of weak spectrum component reconstruction, whispered speech basis and its coefficients are derived by the proposed algorithm,and then they are used to reconstruct the whispered speech.Experimental results show that the proposed algorithm has a better reconstructive performance for weak speech component than that based on Euclidean distance and Kullback-Leibler （K-L）divergence.The reconstructed speech signal gains larger intelligibility improvement by the proposed method.
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