Weighted nonnegative tensor factorization: on monotonicity of multiplicative update rules and application to user-guided audio source separation
type: Dataset
contact point: Organization
  fn: CCSD
keyword: spisignalengineering-sciences-physicssignal-and-image-processing
keyword: infoinfo-tscomputer-science-cssignal-and-image-processing
keyword: infoeu-reposemanticsreport
keyword: weighted-nonnegative-tensor-factorization
keyword: multiplicative-updates
keyword: user-guided-audio-source-separation
keyword: reports
keyword: weighted-nonnegative-matrix-factorization
keyword: monotonicity
landing page: https://inria.hal.science/hal-00878685
description: This report focuses on so-called weighted variants of nonnnegative matrix factorization (NMF) and more generally nonnnegative tensor factorization (NTF) approximations. First, we consider multiplicative update (MU) rules to optimize these approximations, and we prove that some results on monotonicity of MU rules for NMF generalize without restrictions to both the NTF and the WNTF cases. Second, we propose new weighting strategies for an existing NTF-based user-guided audio source separation method. Experimental evaluation shows that these weightings lead to better source separation than the same model without using the weights. The best configuration of the proposed method was entered into the fourth community-based Signal Separation Evaluation Campaign (SiSEC 2013).
issued: 2026-05-05T22:31:12.897674
language: en
modified: 2026-05-05T22:31:12.897678

test_moissonnage_selune
type: Agent

Weighted nonnegative tensor factorization: on monotonicity of multiplicative update rules and application to user-guided audio source separation
type: Distribution
access URL: https://inria.hal.science/hal-00878685
format: HTML
issued: 2026-05-05T22:31:12.915878
modified: 2026-05-05T22:31:12.840985