Higher order tensor model has been seen as a potential mathematical framework to manipulate the multiple factors underlying the observations. In this paper, we propose a flexible two stage algorithm for K-mode Convolutive Nonnegative Tucker Decomposition (K-CNTD) model by an alternating least square procedure. This model can be seen as a convolutive extension of Nonnegative Tucker Decomposition...

Multilinear algebra of the higher-order tensor has been proposed as a potential mathematical framework for machine learning to investigate the relationships among multiple factors underlying the observations. One popular model Nonnegative Tucker Decomposition (NTD) allows us to explore the interactions of different factors with nonnegative constraints. In order to reduce degeneracy problem of t...

This paper studies the problem of blind separation of convolutively mixed source signals on the basis of the joint diagonalization (JD) of power spectral density matrices (PSDMs) observed at the output of the separation system. Firstly, a general framework of JD-based blind source separation (BSS) is reviewed and summarized. Special emphasis is put on the separability conditions of sources and ...

This paper deals with the blind separation problem of MultiInput Multi-Output (MIMO) convolutive mixtures. Previously, we presented some algorithms based on mutually referenced criterion to separate MIMO convolutive mixtures. However, the proposed algorithms are time consuming and they need a lot of computation efforts. It is obvious that the computation efforts can be reduced as well the conve...

A blind separation algorithm utilizing nonstationarity of sources is proposed. It is suitable particularly for separation of such strongly nonstationary signals as voices. The original version of the algorithm was proposed by one of the authors. The present version has made two improvements. First, it is extended to be able to deal with not only instantaneous mixture but also convolutive mixtur...

We propose a new two-stage blind separation and deconvolution (BSD) algorithm for a convolutive mixture of speech, in which a new Single-Input Multiple-Output (SIMO)-model-based ICA (SIMOICA) and blind multichannel inverse filtering are combined. SIMOICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the...

A new approach for convolutive blind source separation (BSS) using penalty functions is proposed in this paper. Motivated by nonlinear programming techniques for the constrained optimization problem, it converts the convolutive BSS into a joint diagonalization problem with unconstrained optimization. Theoretical analyses together with numerical evaluations reveal that the proposed method not on...

We propose a new two-stage blind separation and deconvolution (BSD) algorithm for a convolutive mixture of speech, in which a new Single-Input Multiple-Output (SIMO)-modelbased ICA (SIMO-ICA) and blind multichannel inverse filtering are combined. SIMO-ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at th...