1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
In this letter, we address an audio signal separation problem and propose a new effective algorithm for solving a bilevel optimization in discriminative nonnegative matrix factorization (NMF). Recently, discriminative training of NMF bases has been developed for better signal separation in supervised NMF (SNMF), which exploits a priori training of given sample signals. The optimization in this method consists of a simultaneous minimization of two objective functions, resulting in a bilevel optimization problem with SNMF (BiSNMF), where conventional methods approximately solve this optimization. To strictly solve BiSNMF, we introduce a new algorithm with the following two features: (a) conversion of the optimization constraint into a penalty term and (b) optimization of the reformulated problem on the basis of a multiplicative steepest descent, ensuring the nonnegativity of variables. Experiments on music signal separation show the efficacy of the proposed algorithm.
© Copyright 2019 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.