Name
Abstract | ||
|---|---|---|
In this paper, a new tensor factorization method based on k-SCA [1] approach is developed to solve the underdetermined blind identification (UBI) problem where k sources are active in each signal segment. Similar to k-SCA methods we assume our k is equal to the number of sensors minus one. This approach improves the general upper bound for maximum possible number of sources in a second order underdetermined blind identification method called SOBIUM. The method is applied to the mixtures of synthetic signals and the results are illustrated. Compared to the recently developed SOBIUM approach, the proposed method is able to identify the channels for more number of source signals. Using the estimated mixing channels the separation of sources is also easily possible. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/MLSP.2013.6661973 | MLSP |
Keywords | Field | DocType |
blind source separation,matrix decomposition,tensors,SOBIUM approach,UBI problem,blind source identification,blind source separation,estimated mixing channels,general upper bound,k-SCA approach,second order underdetermined blind identification method,signal segment,source signals,sparse component analysis methods,sparse events,synthetic signal mixtures,tensor factorization method | Underdetermined system,Tensor,Pattern recognition,Computer science,Upper and lower bounds,Matrix decomposition,Communication channel,Artificial intelligence,Tensor factorization,Blind signal separation,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-0363 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bahador Makkiabadi | 1 | 53 | 8.92 |
| Saeid Sanei | 2 | 530 | 72.63 |