Abstract
Additional priors for nonnegative matrix factorization (NMF) are a powerful way of adapting NMF to specific tasks, such as for example audio source separation. For this application, priors supporting sparseness or temporal continuity have been proposed. However, these priors are not helpful for all kinds of signals and should therefore only be used when needed. For some mixtures, only some components of the mixtures should be supported by these priors. We present an easy, but efficient method of adapting priors to different components. We show, that the separation results are improved, while the computational complexity is even slightly reduced. We also show, that our method is a helpful modification for the combination of different priors.
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Becker, J.M., Rohlfing, C. (2015). Component-Adaptive Priors for NMF. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_48
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DOI: https://doi.org/10.1007/978-3-319-22482-4_48
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