Abstract
In this paper we propose a new strategy to separate convolutive mixtures of temporally-white signals. The basic idea is to transform the convolutive mixture in several instantaneous mixtures by using the discrete Fourier transform. Subsequently, each instantaneous mixture is separated using a neural network whose coefficients are adapted by minimizing the mean squared error between its outputs and a desired signal previously obtained using an unsupervised algorithm (like JADE). This new strategy does not suffer from the amplitude/permutation indeterminacies that appear in other frequency-domain approaches.
This work has been supported by Ministerio de Ciencia y Tecnología of Spain and FEDER funds (grant TIC2001-0751-C04-01).
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Dapena, A., Escudero, C. (2003). A novel unsupervised strategy to separate convolutive mixtures in the frequency domain. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_33
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DOI: https://doi.org/10.1007/3-540-44869-1_33
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