Mixing matrix estimation method for unknown number blind separation of sparse sources

A technology of aliasing matrix and blind source separation, applied in computing, computer parts, instruments, etc., can solve problems such as poor estimation accuracy

Inactive Publication Date: 2009-01-07
HARBIN INST OF TECH
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Problems solved by technology

[0011] The present invention aims to solve the problem that the existing aliasing matrix estimation method based on classical clustering algorithm for blind separation of sparse sources requires the number of source signals to be known and the estimation accuracy is poor, and further provides a blind separation of unknown number of sparse sources The aliasing matrix estimation method of

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  • Mixing matrix estimation method for unknown number blind separation of sparse sources
  • Mixing matrix estimation method for unknown number blind separation of sparse sources
  • Mixing matrix estimation method for unknown number blind separation of sparse sources

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specific Embodiment approach 1

[0048] The specific process of the aliasing matrix estimation method for blind separation of unknown number of sparse sources described in this embodiment is as follows:

[0049] Step A, remove the observation data points in the observation signal whose absolute value is less than a predetermined threshold; the observation signal has better sparsity in the time domain;

[0050] Step B, project the remaining observation data points onto the upper half unit hypersphere, and obtain the data set after the observation data points are projected onto the upper half unit hypersphere as Z={z j |j=1, 2,..., L}, where z j is an M-dimensional vector, L is the number of data points;

[0051] Step C, estimate the number of source signals, and obtain the aliasing matrix, the specific process is:

[0052] Step C1. Set the number of source signals as c, and the upper limit of its possible values ​​is c max , select the initial value of the source signal number c as 2;

[0053] Step C2, tak...

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Abstract

The invention relates to an alias matrix estimating method of the blind separation of sparse sources with unknown numbers, which belongs to the engineering field and more particularly relates to the technical field of the blind source separation. The method aims at solving the problems that the existing alias matrix estimating method of the blind separation of the sparse sources on the basis of a classic clustering algorithm requires that the number of source signals is known and the estimating precision is poorer. According to the geometric feature that sparse source alias signals present linear clustering and on the basis of the distance relation of a clustering center and each sort of data dense point, the invention provides a novel clustering validity criterion and estimates the number of the source signals according to the criterion. At the same time, each sort of data dense point is found out by making use of Hough transformation so as to substitute the clustering center to estimate the alias matrix, thereby improving the estimating precision of the alias matrix. The method of the invention is suitable for the estimation for the alias matrix of the blind separation of the sparse sources under the condition that the number of the source signals is unknown and is widely applied to the fields of speech recognition, medical signal processing, wireless communication, and so on, and the estimating precision of the alias matrix is improved.

Description

technical field [0001] The invention belongs to the field of engineering, and specifically relates to the technical field of blind source separation. Background technique [0002] Blind Source Separation (BSS) refers to estimating the source signal only from the aliased signal observed by the sensor according to the statistical characteristics of the input source signal without knowing the prior information of the source signal and the transmission channel. Since blind source separation technology does not require prior knowledge of source signals and transmission channels, it has broad practical applications in many fields such as wireless communication, array signal processing, biomedical signal processing, speech signal processing, and image signal processing. prospect. [0003] At present, the most studied blind source separation model is the linear instantaneous mixed model, as shown in the following formula: [0004] x ( t ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/00
Inventor 彭喜元付宁乔立岩彭宇
Owner HARBIN INST OF TECH
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