NMF-based informed source separation

Abstract

Informed Source Separation (ISS) is a topic unifying the research fields of both source separation and source coding. Its main objective is to recover audio objects out of a mixture with a source separation step assisted by a set of compact parameters extracted with complete knowledge of the sources. ISS can be used for applications such as active listening and remixing of music (e.g. karaoke). In this paper, we propose a new ISS method which includes a semi-blind source separation (SBSS) step in the ISS decoder to decrease the amount of parameter bit rate. SBSS is conducted by factorizing the mixture in time-frequency domain by nonnegative matrix factorization (NMF). The transmitted parameters consist of a compact NMF initialization as well as residuals calculated in the NMF domain. We show in simulations that using SBSS in the decoder increases the separation quality and that our scheme improves the rate-distortion performance in comparison to a state-of-the art method.