Single Channel Blind Source Separation of Polyphonic Signals in Sub-Gaussian Condition
An extension of blindly separating disjointed polyphonic signals by single channel independent component analysis (SCICA) in Sub-Gaussian condition is proposed.Nowadays single channel ICA can only be applied in the condition of mixed signal who has disjointed power spectrum density and source signals arc sparse, it makes polyphonic signal presents part Sub-Gaussian distribution and is hard to blindly separate from Sub-Gaussian environment by single channel ICA. The distribution features (including probability density, kurtosis, power spectrum and signal interference ratio) of source signals, mixed matrix, mixed signals and separated signals are analyzed. When the kurtosis of SubGaussian setting decreases, the SIR of polyphonic signal who exposes Sub-Gaussian distribution reduces sharply whereas the SIR of polyphonic signal who exposes Super-Gaussian distribution changes smoothly. More specifically, when mixed signals only present Gaussian distribution or Sub-Gaussian distribution in Sub-Gaussian condition, the polyphonic signal that shows SubGaussian distribution cannot be separated by single channel ICA.
Single Channel ICA Polyphonic Signal BSS SubGaussian Super-Gaussian
GUO Yina
Department of Electronic and Communication, Taiyuan University of Science and Technology ShanXi Taiyuan 030024, China
国际会议
长沙
英文
2644-2647
2010-03-13（万方平台首次上网日期，不代表论文的发表时间）