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发表刊物：IEEE the Journal of Selected Topics in Signal Processing

摘要：A novel method for adaptive sparsity non-negative matrix factorization is proposed. The proposed factorization decomposes an information-bearing matrix into two-dimensional convolution of factor matrices that represent the spectral dictionary and temporal codes. We derive a variational Bayesian approach to compute the sparsity parameters for optimizing the matrix factorization. The method is demonstrated on separating audio mixtures recorded from a single channel. In addition, we have proven that the extraction of the spectral dictionary and temporal codes is significantly more efficient.

全部作者： S.S. Dlay, W.L. Woo,Bin Gao

通讯作者：Bin Gao

学科门类：工学

卷号：5

期号：5

页面范围：989-1001

是否译文：否

发表时间：2011-08-17