DocumentCode :
2128256
Title :
A blind separation method of instantaneous speech signal via independent components analysis
Author :
Guo, Wei ; Zong, Qingquan
Author_Institution :
Comput. Sch., Wuhan Univ., Wuhan, China
fYear :
2012
fDate :
21-23 April 2012
Firstpage :
3001
Lastpage :
3004
Abstract :
Human speech recognition shows remarkable robustness in a variety of listening conditions, including competing talkers, environmental sounds, and background noise. This kind of distinguishing ability is a specific sensing mechanism capacity which is owned by of the internal understanding mechanism of the human voice, it is known as the "cocktail party effect". Blind Source Separation (BSS) is a method to estimate the original signal by using mixed signals observed, which is based on independent component analysis (ICA). The basic principle is to find the hidden factors or the method to calculate the independent data. From the perspective of linear transformation and linear space, the source signals are independent and non-Gaussian, they can be seen as linear space-based signals, while the observed signal was a linear combination of source signals. This study attempted to isolate the effects that energetic masking, defined as the loss of detectable target information due to the spectral overlap of the target and masking signals, has on multi talker speech perception. we will describe a signal source separation method of two-channel stereo instantaneous mixed signal using independent component analysis The main purpose of ICA is to determine a non-orthogonal transformation when the source signal and linear transformation are both unknown, and makes the transformed output as much as possible statistically independent of the various signal components, thus to estimate the basic structure of space or the source signal in the mixed signals observed. The algorithm of this paper is mainly aimed at the non-positive definite instantaneous mixed voice signal without noise, the blind source separation in frequency domain.
Keywords :
blind source separation; independent component analysis; speech processing; BSS; ICA; background noise; blind source separation method; cocktail party effect; competing talkers; energetic masking; environmental sound; human speech recognition; independent components analysis; instantaneous mixed signal; instantaneous speech signal; linear space-based signal; linear transformation; listening condition; multitalker speech perception; nonorthogonal transformation; sensing mechanism capacity; signal source separation; two-channel stereo; Analytical models; Blind source separation; Decision support systems; Human voice; Independent component analysis; Robustness; Blind Source Separation; Independent Component Analysis; non-positive definite instantaneous mixtures;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on
Conference_Location :
Yichang
Print_ISBN :
978-1-4577-1414-6
Type :
conf
DOI :
10.1109/CECNet.2012.6202044
Filename :
6202044
Link To Document :
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