In this paper, we study the effect of prior information on the quality of informed source separation algorithms. We present results with our system for solo and accompaniment separation and contrast our findings with two other state-of-the art approaches. Results suggest current separation techniques limit performance when compared to extraction process of prior information. Furthermore, we present an alternative view of the separation process where the additivity constraint of the algorithm is removed in the attempt to maximize obtained quality. Plausible future directions in sound separation research are discussed.

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