A separate and restore approach to score-informed music decomposition

Our goal is to improve the perceptual quality of signal components extracted in the context of music source separation. Specifically, we focus on decomposing polyphonic, mono-timbral piano recordings into the sound events that correspond to the individual notes of the underlying composition. Our separation technique is based on score-informed Non-Negative Matrix Factorization (NMF) that has been proposed in earlier works as an effective means to enforce a musically meaningful decomposition of piano music. However, the method still has certain shortcomings for complex mixtures where the tones strongly overlap in frequency and time. As the main contribution of this paper, we propose a restoration stage based on refined Wiener filter masks to score-informed NMF. Our idea is to introduce notewise soft masks created from a dictionary of perfectly isolated piano tones, which are then adapted to match the timbre of the target components. A basic experiment with mixtures of piano tones shows improvements of our novel reconstruction method with regard to perceptually motivated separation quality metrics. A second experiment with more complex piano recordings shows that further investigations into the concept are necessary for real-world applicability.