| Project/Area Number |
18540177
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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 |
Basic analysis
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| Research Institution | Osaka Kyoiku University |
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Principal Investigator |
MORIMOTO Akira Osaka Kyoiku University, Education, Assistant Professor (50239688)
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| Co-Investigator(Kenkyū-buntansha) |
ASHINO Ryuichi Osaka Kyoiku University, Education, Professor (80249490)
MANDAI Takeshi Osaka Electro-Communiration University, Engineering, Professor (10181843)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,650,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥450,000)
Fiscal Year 2007: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2006: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | wavelet / source separation / time-frequency / real analysis / spatial mixtute / snatio-tem noval mixture / time delay / analytic signal / 時空間混合問題 |
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| Research Abstract |
The cocktail party effect is a challenging problem in auditory perception, which deals with the specialized human listening ability to focus one's listening attention on a single talker among a cacophony of conversations and background noise. Our interest is how to build a machine to solve the cocktail party problem in a satisfactory manner. This problem is called blind source separation. The traditional way to solve this problem is an independent component analysis. We have proposed blind source separation methods based on time-frequency information obtained from the continuous wavelet transform. There are three types of blind source separation problems, which are called spatial mixture problem, temporal mixture problem and spatio-temporal mixture problem, respectively.
This research project dealt with the spatial mixture problem and the simplest type of the spatio-temporal mixture problem using wavelet analysis. Our methods have the following three merits.
1. The number of sources is estimated firstly.
2. Using the number of sources, the other parameters are estimated with high accuracy.
3. The errors between estimated sources and real sources are small enough.
We have checked these merits according to numerical simulations. Moreover, in the case of spatial mixture problem, using more than two wavelet transforms, we can estimate model parameters in a noisy environment. In the case of the simplest type of the spatio-temporal mixture problem, the source location problem using time difference of arrival measurements is considered.
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