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首页> 外文期刊>Selected Topics in Signal Processing, IEEE Journal of >Adaptive Sparsity Non-Negative Matrix Factorization for Single-Channel Source Separation
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Adaptive Sparsity Non-Negative Matrix Factorization for Single-Channel Source Separation

机译：用于单通道源分离的自适应稀疏非负矩阵分解

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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 with adaptive sparsity which subsequently leads to better source separation performance. Experimental tests and comparisons with other sparse factorization methods have been conducted to verify the efficacy of the proposed method.
机译：提出了一种新的自适应稀疏非负矩阵分解方法。提出的分解将信息承载矩阵分解为代表频谱字典和时间码的因子矩阵的二维卷积。我们推导了一种变分贝叶斯方法来计算稀疏参数，以优化矩阵分解。在分离单个通道上录制的音频混合中演示了该方法。此外，我们已经证明，利用自适应稀疏性，频谱字典和时间码的提取显着更有效，这随后会带来更好的源分离性能。实验测试和与其他稀疏分解方法的比较已经验证了所提方法的有效性。

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