  In live and studio recordings unexpected sound events often lead to
interferences in the signal. For non-stationary interferences, sound source
separation techniques can be used to reduce the interference level in the
recording. In this context, we present a novel approach combining the strengths
of two algorithmic families: NMF and KAM. The recent KAM approach applies
robust statistics on frames selected by a source-specific kernel to perform
source separation. Based on semi-supervised NMF, we extend this approach in two
ways. First, we locate the interference in the recording based on detected NMF
activity. Second, we improve the kernel-based frame selection by incorporating
an NMF-based estimate of the clean music signal. Further, we introduce a
temporal context in the kernel, taking some musical structure into account. Our
experiments show improved separation quality for our proposed method over a
state-of-the-art approach for interference reduction.
