Presentation | 2013-12-21 Mode Estimation by Mean Shift Learning with Variable Window Width and Gaussian Assumption and The Application to Background Sound Spectrogram Estimation Tomokazu TAGUCHI, Yasunari YOKOTA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | There are many techniques for mode estimation, such as graph spectrum and mean shift. Generally, these conventional techniques have some fundamental problems caused of determination of kernel window width and convergence to local optima. Although some methods have tried to overcome these problems, it was resulted in increasing calculation cost. Introducing assumption that main cluster follows Gaussian distribution loosely, a mean shift technique in which kernel window width can be estimated iteratively and is utilized for mode estimation. This article investigated consistency and unbiasedness of the technique numerically, and applied to background sound spectrogram estimation. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Mode estimation / kernel window width determination / graph spectrogram / mean shift / background sound spectrogram |
Paper # | NC2013-55 |
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Committee | NC |
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Conference Date | 2013/12/14(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Mode Estimation by Mean Shift Learning with Variable Window Width and Gaussian Assumption and The Application to Background Sound Spectrogram Estimation |
Sub Title (in English) | |
Keyword(1) | Mode estimation |
Keyword(2) | kernel window width determination |
Keyword(3) | graph spectrogram |
Keyword(4) | mean shift |
Keyword(5) | background sound spectrogram |
1st Author's Name | Tomokazu TAGUCHI |
1st Author's Affiliation | Faculty of Engineering, Gifu University() |
2nd Author's Name | Yasunari YOKOTA |
2nd Author's Affiliation | Faculty of Engineering, Gifu University |
Date | 2013-12-21 |
Paper # | NC2013-55 |
Volume (vol) | vol.113 |
Number (no) | 374 |
Page | pp.pp.- |
#Pages | 6 |
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