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    高斌

    • 教授 博士生导师
    • 性别:男
    • 毕业院校:英国纽卡斯尔大学
    • 学历:博士研究生毕业
    • 学位:哲学博士学位
    • 在职信息:在岗
    • 所在单位:自动化工程学院
    • 学科:信号与信息处理
      测试计量技术及仪器
      检测技术与自动化装置
    • 办公地点:C2-403
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    Machine Learning Source Separation using Maximum A Posteriori Nonnegative Matrix Factorization

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    发表刊物:IEEE Transactions on Cybernetics

    摘要:A novel unsupervised machine learning algorithm for single channel source separation (SCSS) is presented. The proposed method is based on nonnegative matrix factorization which is optimized under the framework of maximum a posteriori (MAP) probability and Itakura-Saito (IS) divergence. The method enables a generalized criterion for variable sparseness to be imposed onto the solution and prior information to be explicitly incorporated through the basis vectors. In addition, the method is scale invariant where both low and high energy components of a signal are treated with equal importance.

    全部作者: W. L. Woo, Bingo W-K. Ling

    通讯作者:Bin Gao

    学科门类:工学

    卷号:44

    期号:7

    页面范围:1169 – 1179

    是否译文:

    发表时间:2014-06-12