Score informed audio source separation using constrained nonnegative matrix factorization and score synthesis

In this paper we present a new method for musical audio source separation, using the information from the musical score to supervise the decomposition process. An original framework using nonnegative matrix factorization (NMF) is presented, where the components are initially learnt on synthetic signals with temporal and harmonic constraints. A new dataset of multitrack recordings with manually aligned MIDI scores is created (TRIOS), and we compare our separation results with other methods from the literature using the BSS EVAL and PEASS evaluation toolboxes. The results show a general improvement of the BSS EVAL metrics for the various instrumental configurations used