Nonnegative Matrix Factorization With Transform Learning
Résumé
Traditional NMF-based signal decomposition relies on the factor-ization of spectral data, which is typically computed by means of short-time frequency transform. In this paper we propose to relax the choice of a pre-fixed transform and learn a short-time orthogonal transform together with the factorization. To this end, we formulate a regularized optimization problem reminiscent of conventional NMF, yet with the transform as additional unknown parameters, and design a novel block-descent algorithm enabling to find stationary points of this objective function. The proposed joint transform learning and factorization approach is tested for two audio signal processing ex-periments, illustrating its conceptual and practical benefits.
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