Underdetermined blind source separation method applying multiple constraints

An underdetermined blind source separation and multiple technology, applied in the field of signal processing, can solve problems such as unsatisfactory signal sparsity, large error in mixing matrix estimation, and affecting algorithm performance

Inactive Publication Date: 2016-05-04
JIANGSU UNIV OF SCI & TECH
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Problems solved by technology

[0004] At present, most of the research is to use the sparsity of the signal to solve the problem of underdetermined blind separation based on the sparse component analysis algorithm. M. Zibulevsky et al. use the sparse component analysis to propose a two-step method to estimate the mixing matrix and the source signal, the mixing matrix The quality of the estimation directly affects the subsequent signal separation effect; based on the sparse representation, Bofill uses the clustering method and the shortest path method to estimate the mixing matrix and the source signal respectively, and successfully separates six source signals from the two observed mixed signals, but The sparsity requirement of the signal is strong; in view of the fact that the basic NMF algorithm cannot solve the problem of blind sou...

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  • Underdetermined blind source separation method applying multiple constraints
  • Underdetermined blind source separation method applying multiple constraints
  • Underdetermined blind source separation method applying multiple constraints

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Embodiment

[0064] Embodiment: select source signal to be speech signal, select model to be the linear instantaneous mixed model of undetermined blind source separation problem, the step of the present invention is applicable to speech signal and this model, but not limited to speech signal and this model.

[0065] The linear instantaneous mixture model of the underdetermined blind speech signal separation problem can be expressed as:

[0066] x(t)=As(t)+n(t)(1)

[0067] Among them, x(t)=[x 1 (t),x 2 (t),...,x M (t)] T Indicates the observed signal vector whose size is M×1 at time t, A=[a 1 ,a 2 ,...,a N ]∈R M×N (Mi Represents the ith column vector of the mixing matrix, s(t)=[s 1 (t),s 2 (t),...,s N (t)] T Represents the source signal vector of size N×1 at time t, and n(t) represents additive Gaussian noise. Therefore, formula (1) can also be expressed as:

[0068] x ( t ) = A s ...

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Abstract

The invention discloses an underdetermined blind source separation method applying multiple constraints, which is improved on the basis of a traditional blind source separation method to realize a better signal separating effect. The method disclosed by the invention aims to separate mixed signals and comprises the steps of firstly carrying out equalization and whitening to improve the robustness of initial conditions, carrying out multi-constraint limit on a non-negative matrix factorization algorithm and carrying out optimization on an objective function and further improving the signal separating property through a feedback mechanism finally. Therefore, the method disclosed by the invention has the advantages of good factor interpretability and high separated signal purity.

Description

technical field [0001] The invention relates to an underdetermined blind source separation method imposing multiple constraints, belonging to the technical field of signal processing. Background technique [0002] BSS (Blind Source Separation, Blind Source Separation) refers to the technology of recovering the source signal only by the signal received by the sensor when the source signal and the mixing method are unknown. Blind source separation when the number of sensors is less than the number of source signals, called underdetermined blind source separation, is a hot research issue in the field of blind signal processing in recent years. All have broad application prospects. [0003] Among the traditional blind signal separation methods, the ICA algorithm is developed on the basis of the principal component analysis (PCA) method, which is only suitable for overdetermined or positive definite mixed models, and must have certain assumptions and constraints. Nonnegative ma...

Claims

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Application Information

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IPC IPC(8): G06K9/62G10L21/0272G06K9/00
CPCG10L21/0272G06F2218/02G06F18/21343G06F18/2134
Inventor 王敏王艳芳
Owner JIANGSU UNIV OF SCI & TECH
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