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Adaptive Natural Gradient Algorithm for Blind Convolutive Source Separation

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Advances in Neural Networks – ISNN 2007 (ISNN 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4493))

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Abstract

An adaptive natural gradient algorithm for blind source separation based on convolutional mixture model is proposed. The proposed method makes use of cost function as optimum criterion in separation process. The update formula of separation matrix is deduced. The learning steps for blind source separation algorithm are given, and high capability of the proposed algorithm has been demonstrated. The simulations results have shown the validity, practicability and the better performance of the proposed method. This technique is suitable for many applications in real life systems.

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Derong Liu Shumin Fei Zengguang Hou Huaguang Zhang Changyin Sun

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© 2007 Springer Berlin Heidelberg

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Feng, J., Zhang, H., Zhang, T., Yue, H. (2007). Adaptive Natural Gradient Algorithm for Blind Convolutive Source Separation. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_88

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  • DOI: https://doi.org/10.1007/978-3-540-72395-0_88

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72394-3

  • Online ISBN: 978-3-540-72395-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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