一种新的单通道源分离方法

Yan-Bo Lin, Yuan-Shan Lee, Tuan Q. Pham, Tzu-Chiang Tai, Jia-Ching Wang
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引用次数: 1

摘要

单源分离的目的是从混合信号中恢复一个特定的信号。本工作开发了一种新的用于自动语音识别(ASR)系统的源分离方法。该方法基于非负矩阵分解(NMF),广泛应用于单通道源分离。在代价函数中,使用了一个柔性距离αβ-散度。另外,高维空间中的混合信号包含一个低维流形。为了保持这种嵌入结构,在目标函数中加入图正则化约束进行优化。实验结果表明,该方法优于基准方法。
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A novel approach for single channel source separation
The purpose of single source separation is to recover a particular signal from a mixed signal. This work develops a novel source separation method for use with an automatic speech recognition (ASR) system. The proposed method is based on non-negative matrix factorization (NMF), which is extensively used in single channel source separation. In the cost function, a flexible distance, αβ-divergence, is used. Additionally, a mixture signal in high-dimensional space contains a low-dimensional manifold. To preserve this embedded structure, a graph regularization constraint is added to the objective function for optimization. The experimental results thus obtained reveal that the proposed method outperforms baseline methods.
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