Paper Info
Title
Approaching the Time Dependent Cocktail Party Problem with Online Sparse Coding Neural Gas
Abstract
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.
Year
DOI
Venue
2009
10.1007/978-3-642-02397-2_17
WSOM
Keywords
Field
DocType
realistic model,time dependent cocktail party,known ground truth,change rate,artificial data,online sparse coding neural,cocktail party problem,signal-to-noise ratio,online pattern-by-pattern,additive noise,reconstruction performance,signal to noise ratio,neural gas,sparse coding,ground truth
Cocktail party effect,Matrix (mathematics),Neural coding,Computer science,Algorithm,Speech recognition,Reconstruction error,Ground truth,Independent component analysis,Blind signal separation,Neural gas
Conference
Volume
ISSN
Citations 
5629
0302-9743
1
PageRank 
References 
Authors
0.35
14
3
Name
Order
Citations
PageRank
Kai Labusch11138.50
Erhardt Barth265358.33
Thomas Martinetz31462231.48