Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Blind source separation for convolutive mixtures
Signal Processing
Independent component analysis by general nonlinear Hebbian-like learning rules
Signal Processing - Special issue on neural networks
Independent component analysis for identification of artifacts in magnetoencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Optimization by Vector Space Methods
Optimization by Vector Space Methods
Blind Separation of Multiple Speakers in a Multipath Environment
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
Blind identification of FIR channels carrying multiple finite alphabet signals
ICASSP '95 Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02
Subspace methods for the blind identification of multichannel FIRfilters
IEEE Transactions on Signal Processing
Multichannel signal separation: methods and analysis
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Source separation using a criterion based on second-orderstatistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Anechoic Blind Source Separation Using Wigner Marginals
The Journal of Machine Learning Research
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This paper presents a time-domain blind source separation algorithm based on the criterion of the general contrast function. The algorithm extends the efficient fixed-point algorithm for instantaneous mixing models to extract a source from convolutive mixed sources. A new decorrelation procedure is proposed to help the algorithm separate the rest of the sources one by one. This scalable feature enables us to extract only some of the source signals from their convolutive mixtures. Also, the algorithm does not suffer from the problems of frequency domain based algorithms on arbitrary scaling and permutations in different frequency components. Simulations on synthetic data and real-world recordings were used to verify the extended fixed-point algorithm. Results of the simulations show that the algorithm is capable of significantly suppressing the crosstalk of the extracted source signals.