ICA-Based Single Channel Source Separation With Time-Frequency Decomposition
Fragment książki (Rozdział monografii pokonferencyjnej)
MNiSW
20
Poziom I
| Status: | |
| Autorzy: | Mika Dariusz, Budzik Grzegorz, Józwik Jerzy |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 238 - 243 |
| Web of Science® Times Cited: | 3 |
| Scopus® Cytowania: | 3 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | 7th IEEE International Workshop on Metrology for AeroSpace |
| Skrócona nazwa konferencji: | MetroAeroSpace 2020 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 22 czerwca 2020 do 24 czerwca 2020 |
| Miasto konferencji: | Pisa |
| Państwo konferencji: | WŁOCHY |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| This paper relates to the separation of single channel source signals from a single mixed signal by means of independent component analysis (ICA). The proposed idea lies in a time-frequency representation of the mixed signal. Statistically independent time-frequency domain (TFD) components of the mixed signal obtained by ICA are grouped by hierarchical clustering and k-mean partitional clustering. The distance between TFD components is measured with the classical Euclidean distance and the β distance of Gaussian distribution. The proposed method was used to separate source signals from single audio mixes of two- and three-component signals. The separation was performed using algorithms written by the authors in Matlab. The best separation results were obtained with the use of the ß distance of Gaussian distribution, a distance measure based on the knowledge of the probabilistic nature of spectra of original constituent signals of the mixed signal. |
