2020 Fiscal Year Final Research Report
Automatic Generation of Biometric Data Incorporating Individual Characteristics by Deep Learning
| Project/Area Number |
19K22859
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 61:Human informatics and related fields
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| Research Institution | University of Tsukuba |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
堀江 和正 筑波大学, 計算科学研究センター, 助教 (60817112)
塩川 浩昭 筑波大学, 計算科学研究センター, 准教授 (90775248)
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| Project Period (FY) |
2019-06-28 – 2021-03-31
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| Keywords | 生体データ / 深層学習 / 個人特性 |
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| Outline of Final Research Achievements |
This project aimed to develop a style-transformation method for biological signals, enabling us to generate training samples for deep-learning-based signal recognition models. Through the project, we obtained much knowledge about the biological signal styles and the style transformation, although we cannot say that we achieved all the original goals.
The main research outcome includes the development of the noise reduction method NR-GAN for mice electroencephalogram signals. This model is based on generative adversarial neural networks and can learn the noise features without using specific examples of noise reduction. This model is an example of the style transformation, when we consider existence of noise as a style. Another outcome is proposal of a method for improving the feature space construction in the documents style transformation. We found that the Flow-based model helps construction of a more appropriate feature space and contributes to improving the learning efficiency.
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| Free Research Field |
医学分野におけるデータ解析
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では，生体信号のノイズ除去を一種のスタイル変換とみなし，手本となるノイズ除去例なしに実際のマウス生体信号に対するノイズ除去が実現可能であることを，NR-GANの開発により示した．また，文章を対象に，従来手法よりもスタイル変換に適した多変量正規分布に従う特徴空間の獲得を行うことができた．これらの点より，スタイル変換に基づくデータ変換・生成に対して一定の学術的貢献ができたと考える．GANに基づくノイズ除去手法の開発は，ノイズ除去前後の学習サンプルを大量に用意する必要がない点に特徴があり，生体信号を用いる医学・生命科学研究分野等での実際のデータ分析においても貢献できる可能性がある．
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