Underdetermined convolutive source separation using GEM-MU with variational approximated optimum model order NMF2D

Ahmed Al-Tmeme, Wai Lok Woo, Satnam Singh Dlay, Bin Gao

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

An unsupervised machine learning algorithm based on nonnegative matrix factor Two-dimensional deconvolution (NMF2D) with approximated optimum model order is proposed. The proposed algorithm adapted under the hybrid framework that combines the generalized EM algorithm with multiplicative update. As the number of parameters in the NMF2D grows exponentially the number of frequency basis increases linearly, the issues of model-order fitness, initialization, and parameters estimation become ever more critical. This paper proposes a variational Bayesian method to optimize the number of components in the NMF2D by using the Gamma-Exponential process as the observation-latent model. In addition, it is shown that the proposed Gamma-Exponential process can be used to initialize the NMF2D parameters. Finally, the paper investigates the issue and advantages of using different window length. Experimental results for the synthetic convolutive mixtures and live recordings verify the competence of the proposed algorithm.
Original languageEnglish
Pages (from-to)31-45
Number of pages15
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Volume25
Issue number1
Early online date24 Oct 2016
DOIs
Publication statusPublished - Jan 2017

Fingerprint

Dive into the research topics of 'Underdetermined convolutive source separation using GEM-MU with variational approximated optimum model order NMF2D'. Together they form a unique fingerprint.

Cite this