Extended semantic initialization for NMF-based audio source separation

Nonnegative matrix factorization (NMF) is often used for source separation of audio signals. In most of these algorithms, the initialization step of the NMF, which has a strong impact on the separation performance, is based on random values or deterministic methods such as singular value decomposition (SVD). Another deterministic initialization approach, which is used e.g. for score-informed source separation algorithms, makes use of synthesized magnitude spectra of harmonic notes. It was shown that this semantic method leads to good separation results in blind source separation (BSS) as well; not only for harmonic but also for percussive mixtures with some harmonic components. In this paper, we present an extension to the semantic approach to enhance the separation quality for arbitrary audio mixtures. We evaluate this extension in a BSS scenario and compare it to other initialization schemes.