Single-mixture audio source separation by subspace decomposition of Hilbert spectrum
Single-mixture audio source separation by subspace decomposition of Hilbert spectrum
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DOI：
10.1109/tasl.2006.885254
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发表时间：
2007-03-01
影响因子：
--
通讯作者：
Hirose, Keikichi
中科院分区：
文献类型：
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作者：
Molla, Md. Khademul Islam;Hirose, Keikichi
提出了一种新的从混合信号中分离音频源的方法。该方法基于将混合信号的Hilbert谱分解为独立的源子空间。希尔伯特变换结合经验模式分解（EMD）构成了HS，它是非平稳信号的高分辨率时频表示。EMD将任何时域信号表示为称为固有模式函数（IMF）的有限组振荡分量的总和。在计算混合信号和单个IMF分量之间的谱投影之后，投影向量用于通过应用主分量分析（PCA）和独立分量分析（伊卡）来导出一组谱独立基。一种基于Kulback-Leibler散度（KLd）的k均值聚类算法，将独立的基向量分组为混合源内部的分量源的数量。混合信号的HS被投影到由每组基向量所跨越的空间上，从而产生独立的源子空间。通过应用逆变换来重构时域源信号。实验结果表明，该算法可以从单一的混合语音中分离出语音和干扰音。
A novel technique is developed to separate the audio sources from a single mixture. The method is based on decomposing the Hilbert spectrum (HS) of the mixed signal into independent source subspaces. Hilbert transform combined with empirical mode decomposition (EMD) constitutes HS, which is a fine-resolution time-frequency representation of a nonstationary signal. The EMD represents any time-domain signal as the sum of a finite set of oscillatory components called intrinsic mode functions (IMFs). After computing the spectral projections between the mixed signal and the individual IMF components, the projection vectors are used to derive a set of spectral independent bases by applying principal component analysis (PCA) and independent component analysis (ICA). A k-means clustering algorithm based on Kulback-Leibler divergence (KLd) is introduced to group the independent basis vectors into the number of component sources inside the mixture. The HS of the mixed signal'is projected onto the space spanned by each group of basis vectors yielding the independent source subspaces. The time-domain source signals are reconstructed by applying the inverse transformation. Experimental results show that the proposed algorithm performs separation of speech and interfering sound from a single mixture.