Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Neural Computation
Learning Overcomplete Representations
Neural Computation
Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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We show how the "Online Sparse Coding Neural Gas" algorithm can be applied to a more realistic model of the "Cocktail Party Problem". We consider a setting where more sources than observations are given and additive noise is present. Furthermore, we make the model even more realistic, by allowing the mixing matrix to change slowly over time. We also process the data in an online pattern-by-pattern way where each observation is presented only once to the learning algorithm. The sources are estimated immediately from the observations. In order to evaluate the influence of the change rate of the time dependent mixing matrix and the signal-to-noise ratio on the reconstruction performance with respect to the underlying sources and the true mixing matrix, we use artificial data with known ground truth.