A Score-Informed Shift-Invariant Extension Of Complex Matrix Factorization For Improving The Separation Of Overlapped Partials In Music Recordings

Similar to non-negative matrix factorization (NMF), complex matrix factorization (CMF) can be used to decompose a given music recording into individual sound sources. In contrast to NMF, CMF models both the magnitude and phase of a source, which can improve the separation of overlapped partials. However, the shift-invariance for spectral templates enabling NMF-based methods to efficiently model vibrato in music is not available with CMF. Further, the estimation of an entire phase matrix for each source results in a high number of parameters in CMF, which often leads to poor local minima. In this paper we show that score information provides a source of prior knowledge rich enough to stabilize the CMF parameter estimation, without sacrificing its expressive power. As a second contribution, we present a shift-invariant extension to CMF bringing the vibrato-modeling capabilities of NMF to CMF. As our experiments demonstrate our proposed method consistently improves the separation quality for overlapped partials compared to score-informed NMF.