Amicable examples for informed source separation

TL;DR

This paper introduces amicable noise, an imperceptible perturbation that enhances the performance of a pretrained source separation model without using side-information, demonstrating significant improvements and robustness.

Contribution

It proposes a novel adversarial attack method that improves source separation performance by adding amicable noise, without relying on side-information during encoding.

Findings

Improves separation performance by 2.23 dB on average

Robust to signal compression

Effective across multiple models with multi-purpose learning

Abstract

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.