産業応用工学会全国大会講演論文集
Online ISSN : 2424-211X
2013
会議情報

階層型ニューラルネットワークを用いた独立成分分析による信号分離と未学習音声への適応評価
*久木原 健介*和久屋 寛*伊藤 秀昭*福本 尚生*古川 達也
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会議録・要旨集 オープンアクセス

p. 18-19

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Independent component analysis (ICA) is a signal separation technique inspired by the famous psychological phenomenon called cocktail party effect. Various kinds of its applications have been undertaken by a lot of researchers so far, and an alternative method based on a layered neural network with structural pruning was tried in the preceding studies. However, how to develop such a signal separation matrix was the center of attention, so how to apply it after training was not discussed a lot. Then, from the viewpoint of adaptability to untrained signals, some computer simulations are carried out in this study. As a result, it is found experimentaly that a vocal signal separation task with the developed separation matrix is accomplished successfully as we have intended in advance.
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