| Project/Area Number |
17560335
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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 Graduate School of Natural Science and Technology, Professor, 自然科学研究科, 教授 (00207945)
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| Co-Investigator(Kenkyū-buntansha) |
HIRANO Akihiro Graduate School of Natural Science and Technology, Assistant Professor, 自然科学研究科, 講師 (70303261)
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| Project Period (FY) |
2005 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,700,000 (Direct Cost: ¥3,700,000)
Fiscal Year 2006: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2005: ¥2,900,000 (Direct Cost: ¥2,900,000)
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| Keywords | Blind source separation / Feedforward / Feedback / Learning / Nonlinear / Signal distortion / Over complete / Histogram / 畳み込み混合 |
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| Research Abstract |
A feedback approach and its learning algorithm are proposed for the OC-BSS. By using the sensors more than a half of the sources, at least one output can separate a single signal source. This output is fed back to the inputs of the separation block, and is subtracted from the observations, in order to reduce the number of equivalent signal sources. Two kinds of feedback methods are proposed, which are direct subtraction and sample elimination based on histogram of the observed signals and the separated signal above. In the latter process, signal distortion is further suppressed by the spectral suppression technique. The proposed method can improve a signal to interference ratio by 6〜10 dB compared to the conventional methods.
Source separation and signal distortion are theoretically analyzed in blind source separation (BSS) systems implemented in both the time and the frequency domains. Feedforward (FF-) BSS systems have some degree of freedom in the solution space. Therefore, signal distortion is likely to occur. Next, a condition for complete separation and distortion free is derived for multi-channel FF-BSS systems. This condition is incorporated in learning algorithms as a distortion free constraint. Computer simulations using speech signals and stationary colored signals are performed for conventional methods and the new learning algorithms employing the proposed distortion free constraint. The proposed method can drastically suppress signal distortion, while maintaining a high separation performance.
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