NDLI: Single Channel Blind Source Separation of Polyphonic Signals in Sub-Gaussian Condition

Content Provider	IEEE Xplore Digital Library
Author	Guo Yina
Copyright Year	2010
Abstract	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 are 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 Sub-Gaussian 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 Sub-Gaussian distribution cannot be separated by single channel ICA.
Starting Page	390
Ending Page	393
File Size	210662
Page Count	4
File Format	PDF
ISBN	9781424450015
e-ISBN	9781424457397
DOI	10.1109/ICMTMA.2010.74
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Source separation Mechatronics Automation BSS Independent component analysis Interference Blind source separation Sub-Gaussian Equations Acoustic noise Gaussian noise Working environment noise Super-Gaussian Polyphonic Signal Single Channel ICA
Content Type	Text
Resource Type	Article

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