Blind source separation of real world signals
- 22 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 4, 2129-2134
- https://doi.org/10.1109/icnn.1997.614235
Abstract
We present a method to separate and deconvolve sources which have been recorded in real environments. The use of noncausal FIR filters allows us to deal with nonminimum mixing systems. The learning rules can be derived from different viewpoints such as information maximization, maximum likelihood and negentropy which result in similar rules for the weight update. We transform the learning rule into the frequency domain where the convolution and deconvolution property becomes a multiplication and division operation. In particular the FIR polynomial algebra techniques as used by Lambert present an efficient tool to solve true phase inverse systems allowing a simple implementation of noncausal filters. The significance of the methods is shown by the successful separation of two voices and separating a voice that has been recorded with loud music in the background. The recognition rate of an automatic speech recognition system is increased after separating the speech signals.Keywords
This publication has 10 references indexed in Scilit:
- Blind separation of multiple speakers in a multipath environmentPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Infomax and maximum likelihood for blind source separationIEEE Signal Processing Letters, 1997
- Equivariant adaptive source separationIEEE Transactions on Signal Processing, 1996
- Multidimensional density shaping by sigmoidsIEEE Transactions on Neural Networks, 1996
- Multichannel signal separation: methods and analysisIEEE Transactions on Signal Processing, 1996
- Blind separation of instantaneous mixture of sources via an independent component analysisIEEE Transactions on Signal Processing, 1996
- An Information-Maximization Approach to Blind Separation and Blind DeconvolutionNeural Computation, 1995
- Robust learning algorithmfor blind separation of signalsElectronics Letters, 1994
- Independent component analysis, A new concept?Signal Processing, 1994
- Fast implementations of LMS adaptive filtersIEEE Transactions on Acoustics, Speech, and Signal Processing, 1980