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タイトル: A distortion free learning algorithm for feedforward BSS and its comparative study with feedback BSS
シリーズ番号/レポート番号: IJCNN2006
著者: Horita, Akihide
Nakayama, Kenji link image
Hirano, Akihiro link image link image
Dejima, Yasuhiro
中山, 謙二
平野, 晃宏
ナカヤマ, ケンジ
発行日: 2006年 7月
出版社(者): IEEE = Institute of Electrical and Electronics Engineers
雑誌名: IEEE International Conference on Neural Networks - Conference Proceedings, IJCNN2006, Vancouver
開始ページ: 7642
終了ページ: 7649
抄録: Source separation and signal distortion are theoretically analyzed for the FF-BSS systems implemented in both the time and frequency domains and the FB-BSS system. The FF-BSS systems have some degree of freedom, and cause some signal distortion. The FB-BSS has a unique solution for complete separation and distortion free. Next, the condition for complete separation and distortion free is derived for the FF-BSS systems. This condition is applied to the learning algorithms. Computer simulations by using speech signals and stationary colored signals are carried out for the conventional methods and the new learning algorithms employing the proposed distortion free constraint. The proposed method can drastically suppress signal distortion, while maintaining high separation performance. The FB-BSS system also demonstrates good performances. The FF-BSS systems and the FB-BSS system are compared based on the transmission time difference in the mixing process. Location of the signal sources and the sensors are rather limited in the FB-BSS system. © 2006 IEEE.
URI: http://hdl.handle.net/2297/18165
資料種別: Conference Paper
版表示: publisher

このアイテムを引用あるいはリンクする場合は次の識別子を使用してください。 http://hdl.handle.net/2297/18165



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