Introducing A Simple Fusion Framework For Audio Source Separation
Xabier Jaureguiberry, GaŽl Richard, Pierre Leveau, Romain Hennequin, Emmanuel Vincent

We propose in this paper a simple fusion framework for underdetermined 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.