  This paper deals with the problem of informed source separation (ISS), where
the sources are accessible during the so-called \textit{encoding} stage.
Previous works computed side-information during the encoding stage and source
separation models were designed to utilize the side-information to improve the
separation performance. In contrast, in this work, we improve the performance
of a pretrained separation model that does not use any side-information. To
this end, we propose to adopt an adversarial attack for the opposite purpose,
i.e., rather than computing the perturbation to degrade the separation, we
compute an imperceptible perturbation called amicable noise to improve the
separation. Experimental results show that the proposed approach selectively
improves the performance of the targeted separation model by 2.23 dB on average
and is robust to signal compression. Moreover, we propose multi-model
multi-purpose learning that control the effect of the perturbation on different
models individually.
