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非線形混合過程に対する縦続形BSSの学習法の改善と分離特性の解析
http://hdl.handle.net/2297/18414
http://hdl.handle.net/2297/18414c01fed01-a09d-4a30-b9a1-389713404c5e
名前 / ファイル | ライセンス | アクション |
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TE-PR-NAKAYAMA-K-7.pdf (437.2 kB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2017-10-03 | |||||
タイトル | ||||||
タイトル | 非線形混合過程に対する縦続形BSSの学習法の改善と分離特性の解析 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Learning Algorithm Improvement and Source Separation Analysis in Cascade BSS Applied to Nonlinear Mixture | |||||
言語 | ||||||
言語 | jpn | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者 |
竹多, 裕也
× 竹多, 裕也× 中山, 謙二× 平野, 晃宏 |
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提供者所属 | ||||||
内容記述タイプ | Other | |||||
内容記述 | 金沢大学理工研究域 電子情報学系 | |||||
書誌情報 |
電子情報通信学会技術研究報告. SIP, 信号処理 = IEICE technical report 巻 106, 号 476, p. 7-12, 発行日 2007-01-01 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 0913-5685 | |||||
NCID | ||||||
収録物識別子タイプ | NCID | |||||
収録物識別子 | AA11943613 | |||||
出版者 | ||||||
出版者 | IEICE The Institute of Electronics, Information and Communication Engineers | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | 信号源の統計的性質を利用して,混成信号から信号源を分離する方法をブラインド信号原分離という.実際の混合過程では非線形歪みを生じる場合も多く,分離がより複雑になる.このような場合に対して分離と線形化を縦続に構成する方法が提案されている.本研究では,この縦続形構成における線形化に対して安定な学習法,すなわち,初期値の設定法及びパラメータの学習法に関して新しい方法を提案し,非線形成分を大幅に抑制できた.次に,センサー数と分離性能の関係をクロス項s_is_j,i=⃥jの大きさに基づいて解析した.観測信号において主信号が干渉信号の2倍程度あればセンサー数を低減できること,干渉信号が大きい場合は分離が難しい問題であり,センサー数の低減も難しいこと,センサー数を低減した場合は学習が遅くなることを確認した. A cascade form blind source separation (BSS) has been proposed for nonlinear mixture. It consists of a signal separation block and a linearization block in this order from the input to the output. In this paper, firstly, a learning process for the linearization block is improved. New ideas are introduced for initialization and adjusting of parameters. Performance of linearization is well improved. Furthermore, by ordering the new linearization block and the signal separation block in this order, the number of sensors can be well reduced. For instance, 3 sensors are enough to 3 sources. Secondly, a relation between separation performance and the number of sensors is analyzed based on the cross term components s_is_j, i=⃥j. As the learning makes a progress, separation performances for the different number of sensors approach to the same value. In difficult problems, separation performances are affected by the number of sensors. | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
関連URI | ||||||
識別子タイプ | URI | |||||
関連識別子 | http://www.ieice.org/jpn/index.html | |||||
関連URI | ||||||
識別子タイプ | URI | |||||
関連識別子 | http://ci.nii.ac.jp/naid/110006204638 |