Name
Abstract | ||
|---|---|---|
A computationally efficient feature, called Minimum Energy Density (MED) was applied to discriminate audio signals between speech and music in the radio stations programs. The presented binary classifier is based on testing two features: energy distribution and differences between energy in channels. We analyzed 240 hours of signals, from 10 Polish radio stations. Our analysis enables us to provide information about content of particular radio stations. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/IWSSIP.2017.7965606 | 2017 International Conference on Systems, Signals and Image Processing (IWSSIP) |
Keywords | Field | DocType |
speech/music discrimination,sound classification,radio content analysis | Speech processing,Audio signal,Multiple signal classification,Pattern recognition,Binary classification,Computer science,Communication channel,Speech recognition,Energy density,Artificial intelligence,Frequency modulation,Energy distribution | Conference |
ISSN | ISBN | Citations |
2157-8672 | 978-1-5090-6345-1 | 1 |
PageRank | References | Authors |
0.34 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stanisław Kacprzak | 1 | 16 | 2.73 |
| Blazej Chwiecko | 2 | 1 | 0.34 |
| Bartosz Ziólko | 3 | 46 | 15.76 |