Single-Channel Signal Separation Using Spectral Basis Correlation with Sparse Nonnegative Tensor Factorization

Phetcharat Parathai, Naruephorn Tengtrairat, Wai Lok Woo, Bin Gao

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
11 Downloads (Pure)

Abstract

A novel approach for solving the single-channel signal separation is presented the proposed sparse nonnegative tensor factorization under the framework of maximum a posteriori probability and adaptively fine-tuned using the hierarchical Bayesian approach with a new mixing mixture model. The mixing mixture is an analogy of a stereo signal concept given by one real and the other virtual microphones. An “imitated-stereo” mixture model is thus developed by weighting and time-shifting the original single-channel mixture. This leads to an artificial mixing system of dual channels which gives rise to a new form of spectral basis correlation diversity of the sources. Underlying all factorization algorithms is the principal difficulty in estimating the adequate number of latent components for each signal. This paper addresses these issues by developing a framework for pruning unnecessary components and incorporating a modified multivariate rectified Gaussian prior information into the spectral basis features. The parameters of the imitated-stereo model are estimated via the proposed sparse nonnegative tensor factorization with Itakura–Saito divergence. In addition, the separability conditions of the proposed mixture model are derived and demonstrated that the proposed method can separate real-time captured mixtures. Experimental testing on real audio sources has been conducted to verify the capability of the proposed method.

Original languageEnglish
Pages (from-to)5786-5816
JournalCircuits, Systems, and Signal Processing
Volume38
Issue number12
Early online date6 Jun 2019
DOIs
Publication statusPublished - 1 Dec 2019

Fingerprint

Dive into the research topics of 'Single-Channel Signal Separation Using Spectral Basis Correlation with Sparse Nonnegative Tensor Factorization'. Together they form a unique fingerprint.

Cite this