Convolutive blind signal separation method based on multi-target optimization joint block diagonalization

A technology of joint block diagonalization and multi-objective optimization, applied in the field of signal processing, can solve problems such as inability to separate source signals, and achieve the effect of less calculation time and high separation efficiency

Active Publication Date: 2015-09-23
XIDIAN UNIV
View PDF4 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although this method uses the construction cost function to solve the three groups of matrix factors, which reduces the computational complexity and overcomes the shortcomings of being sensitive to noise and prone to singular solutions, but still

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Convolutive blind signal separation method based on multi-target optimization joint block diagonalization
  • Convolutive blind signal separation method based on multi-target optimization joint block diagonalization
  • Convolutive blind signal separation method based on multi-target optimization joint block diagonalization

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0047] The present invention will be further described below in conjunction with accompanying drawing:

[0048] This embodiment is based on a scene of a reception, and uses the present invention to separate the voice of the guest from the content of the guest's conversation received by the microphone and a lot of background noise. In this embodiment, the sensor is a microphone, and the received convolution and aliasing signal is a speech signal.

[0049] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0050] Step 1, Obtain observation data.

[0051] Receive convolutional aliasing signal data from the sensor, in order to process the data in an orderly manner, sequentially number the sensors used, and rearrange the convolutional aliasing signal data into the observation data vector X according to the sequence of sensor numbers i (t).

[0052] Step 2, calculating the second-order delay correlation matrix.

[0053] Calculate th...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a convolutive blind signal separation method based on multi-target optimization joint block diagonalization, mainly solving the problem that in the prior art, not all source signals can be accurately separated from convolutive aliasing signals. The method comprises the steps of: (1) obtaining observation data; (2) calculating the second-order delay correlation matrix of the observation data; (3) constructing a block diagonalization matrix and dividing into submatrixes; (4) establishing a multi-target optimization model about the block diagonalization matrix; (5) estimating the block diagonalization matrix in dependence on the multi-target optimization model; (6) determining whether the difference absolute value between twice block diagonalization matrix evaluated errors is larger than an iteration termination threshold, and if yes, outputting the block diagonalization matrix, and if no, returning to the step (5); and (7) utilizing the block diagonalization matrix to separate source signals from observation signals. The method can accurately separate all source signals from convolutive aliasing signals, has the characteristics of low complexity and high separation efficiency, and can be used for processing voice signals and communication signals.

Description

technical field [0001] The invention relates to the technical field of signal processing, and further relates to a convolution blind signal separation method, which can be used to realize the processing of voice signals and communication signals. Background technique [0002] Blind signal processing has a wide range of applications in various fields such as wireless communication, radar and sonar, image restoration, and speech enhancement. Blind signal separation is an important part of blind signal processing. Its only assumption is that the source signals are statistically independent from each other, which is approximately true for most situations in nature, and the blind signal separation technique does not require any And the prior information of the transmission channel makes this technique a very practical signal processing method. Especially when it is difficult or impossible to model the transmission channel from multiple sources to the sensor, blind signal separat...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G10L21/0272G10L21/0308
Inventor 张伟涛郭交杨若男楼顺天
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap