K-hyperline clustering learning for sparse component analysis
Signal Processing
Comparing measures of sparsity
IEEE Transactions on Information Theory
Fuzzy optical flow using in error concealment
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Correlation-based amplitude estimation of coincident partials in monaural musical signals
EURASIP Journal on Audio, Speech, and Music Processing
A multistage approach to blind separation of convolutive speech mixtures
Speech Communication
Block-sparse recovery via redundant block OMP
Signal Processing
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Convolutive blind source separation (CBSS) that exploits the sparsity of source signals in the frequency domain is addressed in this paper. We assume the sources follow complex Laplacian-like distribution for complex random variable, in which the real part and imaginary part of complex-valued source signals are not necessarily independent. Based on the maximum a posteriori (MAP) criterion, we propose a novel natural gradient method for complex sparse representation. Moreover, a new CBSS method is further developed based on complex sparse representation. The developed CBSS algorithm works in the frequency domain. Here, we assume that the source signals are sufficiently sparse in the frequency domain. If the sources are sufficiently sparse in the frequency domain and the filter length of mixing channels is relatively small and can be estimated, we can even achieve underdetermined CBSS. We illustrate the validity and performance of the proposed learning algorithm by several simulation examples.