Single channel audio source separation
WSEAS Transactions on Signal Processing
Ion-Selective Electrode Array Based on a Bayesian Nonlinear Source Separation Method
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Blind Spectral-GMM Estimation for Underdetermined Instantaneous Audio Source Separation
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery
IEEE Transactions on Signal Processing
An iterative Bayesian algorithm for sparse component analysis in presence of noise
IEEE Transactions on Signal Processing
Gamma Markov random fields for audio source modeling
IEEE Transactions on Audio, Speech, and Language Processing
Compressed sensing and source separation
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Two improved sparse decomposition methods for blind source separation
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Complex nonconvex lp norm minimization for underdetermined source separation
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Bayesian orthogonal component analysis for sparse representation
IEEE Transactions on Signal Processing
Learning sparse representation using iterative subspace identification
IEEE Transactions on Signal Processing
Speech enhancement using Gaussian scale mixture models
IEEE Transactions on Audio, Speech, and Language Processing
Adaptive langevin sampler for separation of t-distribution modelled astrophysical maps
IEEE Transactions on Image Processing
Optimal filter designs for separating and enhancing periodic signals
IEEE Transactions on Signal Processing
Blind extraction of the sparsest component
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Bayesian compressive sensing for cluster structured sparse signals
Signal Processing
Blind separation of non-stationary sources using continuous density hidden Markov models
Digital Signal Processing
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We present a Bayesian approach for blind separation of linear instantaneous mixtures of sources having a sparse representation in a given basis. The distributions of the coefficients of the sources in the basis are modeled by a Student t distribution, which can be expressed as a scale mixture of Gaussians, and a Gibbs sampler is derived to estimate the sources, the mixing matrix, the input noise variance and also the hyperparameters of the Student t distributions. The method allows for separation of underdetermined (more sources than sensors) noisy mixtures. Results are presented with audio signals using a modified discrete cosine transform basis and compared with a finite mixture of Gaussians prior approach. These results show the improved sound quality obtained with the Student t prior and the better robustness to mixing matrices close to singularity of the Markov chain Monte Carlo approach