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Accurate, fast and stable denoising source separation algorithms

By Dr Harri Valpola and Mr Jaakko Särelä

Abstract

Denoising source separation is a recently introduced framework for building source separation algorithms around denoising procedures. Two developments are reported here. First, a new scheme for accelerating and stabilising convergence by controlling step sizes is introduced. Second, a novel signal-variance based denoising function is proposed. Estimates of variances of different source are whitened which actively promotes separation of sources. Experiments with artificial data and real magnetoencephalograms demonstrate that the developed algorithms are accurate, fast and stable

Topics: Statistical Models, Machine Learning, Neural Nets, Artificial Intelligence
Year: 2004
DOI identifier: 10.1007/978-3-540-30110-3_9
OAI identifier: oai:cogprints.org:3637

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