A novel unsupervised strategy to separate convolutive mixtures in the frequency domain

Authors:
Adriana Dapena;Carlos Escudero
Affiliations:
Departamento de Electrónica e Sistemas, Universidade da Coruña, A Coruña, SPAIN 15.071;Departamento de Electrónica e Sistemas, Universidade da Coruña, A Coruña, SPAIN 15.071
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

Citing 3
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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.