2004 Fiscal Year Final Research Report Summary
Blind Source Separation and Estimation Methods for Nonlinear Convoltive Mixtures
| Project/Area Number |
15560323
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Communication/Network engineering
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| Research Institution | Kanazawa University |
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Principal Investigator |
NAKAYAMA Kenji Kanazawa University, Graduate School of Natural Science and Technology, Prof., 自然科学研究科, 教授 (00207945)
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| Co-Investigator(Kenkyū-buntansha) |
HIRANO Akihiro Kanazawa University, Graduate School of Natural Science and Technology, Assistant Prof., 自然科学研究科, 講師 (70303261)
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| Project Period (FY) |
2003 – 2004
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| Keywords | Signal source separation / Mixture process / Nonlinear / Learning algorithm / Signal distortion / Feedforward / Feedback / Independency |
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| Research Abstract |
In practical applications of BSS, processes of generating mixing and sensing signals include nonlinearity, caused by loud speakers, microphones, amplifiers and so on. BSS, cascading a signal group separation block and a linearization block has been proposed for low-order nonlinear mixtures. In the separation block, the signal sources are separated into each group, including its high-order components. The high-order components are further suppressed through the linearization block.
In this report, separation performance of the nonlinear BSS is analyzed from several view points. The number of the sensors is increased from that of the signal sources in order to cancel the interference. Moreover, the interference components is decided by a ratio of the nonlinear and the linear components. A relation between the ratio of the components and the number of the sensors is analyzed. The number of the sensors can be reduced when the ratio of the nonlinearity is small. And a Cascade Form BSS Connecting Linearization and Source Separation and Linearization is analyzed.
Next, effects of the initial guess of the separation matrix is analyzed. The training was carried out using 50 independent random initial guess, and good separation is obtained by a 25% probability. Moreover, effect of including 3rd-order terms is analyzed. When the 3rd-order term is under 10%, good separation performance can be obtained.
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