Name
Abstract | ||
|---|---|---|
Separation of independent sources using independent component analysis (ICA) requires prior knowledge of the number of independent sources. Performing ICA when the number of recordings is greater than the number of sources can give erroneous results. To improve the quality of separation, the most suitable recordings have to be identified before performing ICA. Techniques employed to estimate suitable recordings require estimation of number of independent sources or require repeated iterations. However there is no objective measure of the number of independent sources in a given mixture. Here, a technique has been developed to determine the number of independent sources in a given mixture. This paper demonstrates that normalised determinant of the global matrix is a measure of the number of independent sources, N, in a mixture of M recordings. It has also been shown that performing ICA on N randomly selected recordings out of M recordings gives good quality of separation. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1155/2009/694850 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
suitable recording,objective measure,independent component analysis,good quality,m recording,global matrix,undercomplete mixture,prior knowledge,independent source,normalised determinant,erroneous result,signal processing | Signal processing,Pattern recognition,Computer science,Iterative method,Matrix (mathematics),Speech recognition,Artificial intelligence,Independent component analysis,Quantum information,Source separation,Machine learning | Journal |
Volume | Issue | ISSN |
2009, | 1 | 1687-6180 |
Citations | PageRank | References |
23 | 0.95 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ganesh R. Naik | 1 | 298 | 25.37 |
| Dinesh K. Kumar | 2 | 83 | 9.17 |