Amicable examples for informed source separation
This work addresses a specific challenge in informed source separation for audio processing, offering an incremental improvement over existing methods.
The paper tackles the problem of improving a pretrained source separation model without using side-information by introducing an imperceptible perturbation called amicable noise, resulting in an average performance improvement of 2.23 dB for the targeted model.
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.