Parathai P, Woo W L, Dlay S S, Gao Bin
School of Electrical and Electronic Engineering, Newcastle University, England, United Kingdom
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
J Acoust Soc Am. 2015 Jan;137(1):EL124-9. doi: 10.1121/1.4903913.
An innovative method of single-channel blind source separation is proposed. The proposed method is a complex-valued non-negative matrix factorization with probabilistically optimal L1-norm sparsity. This preserves the phase information of the source signals and enforces the inherent structures of the temporal codes to be optimally sparse, thus resulting in more meaningful parts factorization. An efficient algorithm with closed-form expression to compute the parameters of the model including the sparsity has been developed. Real-time acoustic mixtures recorded from a single-channel are used to verify the effectiveness of the proposed method.
提出了一种创新的单通道盲源分离方法。所提出的方法是一种具有概率最优L1范数稀疏性的复值非负矩阵分解。这保留了源信号的相位信息，并强制时间码的固有结构具有最优稀疏性，从而得到更有意义的部分分解。已开发出一种具有闭式表达式的高效算法来计算包括稀疏性在内的模型参数。使用从单通道记录的实时声学混合信号来验证所提方法的有效性。
