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Frequency domain convolution blind signal separation method based on multi-objective optimization

A multi-objective optimization and blind signal separation technology, applied in the field of blind signal processing, can solve problems such as easy convergence to degradation, and achieve the effect of improving reliability

Active Publication Date: 2018-08-03
XIDIAN UNIV
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Problems solved by technology

[0007] The purpose of the present invention is to overcome the deficiencies in the above-mentioned prior art, and propose a frequency-domain convolution blind signal separation method based on multi-objective optimization, which is used to solve the problem existing in the prior art that is easy to converge to a degenerate solution, and Blind signal separation of frequency-domain convolution that can realize source signals less than the number of observation signals

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  • Frequency domain convolution blind signal separation method based on multi-objective optimization
  • Frequency domain convolution blind signal separation method based on multi-objective optimization
  • Frequency domain convolution blind signal separation method based on multi-objective optimization

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Embodiment Construction

[0052] Below in conjunction with accompanying drawing and specific embodiment, the present invention is described in further detail:

[0053] This embodiment is based on a cocktail party scene, the conversation contents of three people received from four microphone sensors, and the conversation voices of three people are separated by using the present invention. In this implementation example, the sensor is a microphone, and the received convolution and aliasing signal is a speech signal.

[0054] refer to figure 1 , a frequency domain convolution blind signal separation method based on multi-objective optimization, including the following steps:

[0055] Step 1) Get the set of target matrices

[0056] (1a) M electrical signal sensors receive observation signals x from N source signal sensors m (t), form the observed signal vector x(t), x(t)=[x 1 (t),...,x M (t)] T , where, N≥1, and M≥N, m represents the serial number of the sensor, m=1,...,M, in this implementation ex...

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Abstract

The invention provides a frequency domain convolution blind signal separation method based on multi-objective optimization, which is used for solving the problem that convergence to regressive solution easily occurs in the prior art, and can realize the frequency domain convolution blind signal separation with the number of source signals being smaller than that of observation signals. The methodcomprises the following implementation steps: acquiring a target matrix set R; constructing a diagonalizable matrix B (omega k); constructing a non-orthogonal joint diagonalizable multi-objective optimization model; by utilizing the non-orthogonal joint diagonalizable multi-objective optimization model, estimating the disjunct matrix W (omega k) on each frequency point of the target matrix set R;and acquiring the estimated value of time domain source signals. The method is high in reliability and wide in application range, and can be applied to blind separation of the convolution mixed signals including voice signals, communication signals and the like under over-determined conditions.

Description

technical field [0001] The invention belongs to the technical field of blind signal processing, and relates to a frequency domain convolution blind signal separation method, in particular to a frequency domain convolution blind signal separation method based on multi-objective optimization joint diagonalization, which can be applied under overdetermined conditions Blind separation of convolutional mixtures of speech signals, communication signals, etc. Background technique [0002] The objective optimization problem generally refers to obtaining the optimal solution of the objective function through a certain optimization algorithm. When the optimized objective function is one, it is called Single-objective Optimization Problem (SOP). When there are two or more objective functions to be optimized, it is called multi-objective optimization (Multi-objective Optimization Problem, MOP). Unlike single-objective optimization, which is a finite solution, the solution of multi-obj...

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Application Information

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IPC IPC(8): G10L21/0272G06F17/16
CPCG06F17/16G10L21/0272
Inventor 张伟涛孙瑾铃李扬楼顺天
Owner XIDIAN UNIV
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