Name
Abstract | ||
|---|---|---|
We propose in this paper a simple fusion framework for un-derdetermined audio source separation. This framework can be applied to a wide variety of source separation algorithms providing that they estimate time-frequency masks. Fusion principles have been successfully implemented for classification tasks. Although it is similar to classification, audio source separation does not usually take advantage of such principles. We thus introduce some general fusion rules inspired by classification and we evaluate them in the context of voice extraction. Experimental results are promising as our proposed fusion rule can improve separation results up to 1 dB in SDR. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/MLSP.2013.6661930 | Machine Learning for Signal Processing |
Keywords | Field | DocType |
audio signal processing,sensor fusion,signal classification,source separation,SDR,classification tasks,fusion framework,fusion principles,general fusion rules,time-frequency masks,underdetermined audio source separation,voice extraction,audio source separation,data fusion,machine learning,nonnegative matrix factorization | Computer science,Fusion,Signal classification,Artificial intelligence,Audio signal processing,Blind signal separation,Source separation,Pattern recognition,Fusion rules,Sensor fusion,Speech recognition,Non-negative matrix factorization,Machine learning | Conference |
ISSN | Citations | PageRank |
1551-2541 | 4 | 0.43 |
References | Authors | |
17 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaureguiberry, X. | 1 | 4 | 0.43 |
| Richard G. F. Visser | 2 | 9 | 3.50 |
| Leveau, P. | 3 | 4 | 0.43 |
| Hennequin, R. | 4 | 4 | 0.43 |