Title
Supervised nonnegative matrix factorization with Dual-Itakura-Saito and Kullback-Leibler divergences for music transcription.
Abstract
In this paper, we present a convex-analytic approach to supervised nonnegative matrix factorization (SNMF) based on the Dual-Itakura-Saito (Dual-IS) and Kullback-Leibler (KL) divergences for music transcription. The Dual-IS and KL divergences define convex fidelity functions, whereas the IS divergence defines a nonconvex one. The SNMF problem is formulated as minimizing the divergence-based fidelity function penalized by the l(1) and row-block l(1) norms subject to the nonnegativity constraint. Simulation results show that (i) the use of the Dual-IS and KL divergences yields better performance than the squared Euclidean distance and that (ii) the use of the Dual-IS divergence prevents from false alarms efficiently.
Year
Venue
Field
2016
European Signal Processing Conference
Signal processing,Fidelity,Combinatorics,Divergence,Euclidean distance,Regular polygon,Squared euclidean distance,Non-negative matrix factorization,Kullback–Leibler divergence,Mathematics
DocType
ISSN
Citations 
Conference
2076-1465
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Hideaki Kagami100.68
Masahiro Yukawa227230.44