Nonnegative Matrix Factorization with Transform Learning
نویسندگان
چکیده
Traditional NMF-based signal decomposition relies on the factorization of spectral data which is typically computed by means of the short-time Fourier transform. In this paper we propose to relax the choice of a pre-fixed transform and learn a short-time unitary transform together with the factorization, using a novel block-descent algorithm. This improves the fit between the processed data and its approximation and is in turn shown to induce better separation performance in a speech enhancement experiment.
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عنوان ژورنال:
- CoRR
دوره abs/1705.04193 شماره
صفحات -
تاریخ انتشار 2017