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- W4300342206 abstract "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." @default.
- W4300342206 created "2022-10-03" @default.
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- W4300342206 date "2016-09-20" @default.
- W4300342206 modified "2023-10-12" @default.
- W4300342206 title "Interference Reduction in Music Recordings Combining Kernel Additive Modelling and Non-Negative Matrix Factorization" @default.
- W4300342206 doi "https://doi.org/10.48550/arxiv.1609.06210" @default.
- W4300342206 hasPublicationYear "2016" @default.
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