Informed source separation from monaural music with limited binary time-frequency annotation

This paper presents a novel informed audio source separation algorithm given a limited binary time-frequency annotation. Assuming that all the sources can be represented using a low-rank model, we derive an objective function to minimize the rank of the source spectrogram, and the error between the target and the estimated coefficients. Especially, we apply the nuclear norm and l₁-norm, which allow a relaxation of the model, and represent them in the convex formulation. Experimental results show that the proposed method achieves better and more robust separation performance than the state-of-the-art under the incomplete and inexact annotation condition.