Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Advances in Speech Signal Processing
Advances in Speech Signal Processing
Estimation of Shape Parameter of GGD Function by Negentropy Matching
Neural Processing Letters
Blind Separation of Speech by Fixed-Point ICA with Source Adaptive Negentropy Approximation
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Post-processing for frequency-domain blind source separation in hearing aids
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
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This paper presents a novel method for the enhancement of independent components of mixed speech signal segregated by the frequency domain independent component analysis (FDICA) algorithm. The enhancement algorithm proposed here is based on maximum a posteriori (MAP) estimation of the speech spectral components using generalized Gaussian distribution (GGD) function as the statistical model for the time-frequency series of speech (TFSS) signal. The proposed MAP estimator has been used and evaluated as the post-processing stage for the separation of convolutive mixture of speech signals by the fixed-point FDICA algorithm. It has been found that the combination of separation algorithm with the proposed enhancement algorithm provides better separation performance under both the reverberant and non-reverberant conditions.