This paper presents a robust and precise method for solving the permutation problem of frequency-domain blind source separation. It is based on two previous approaches: the direction of arrival estimation and the inter-frequency correlation. We discuss the advantages and disadvantages of the two approaches, and integrate them to exploit their respective advantages. We also present a closed form formula to estimate the directions of source signals from a separating matrix obtained by ICA. Experimental results show that our method solved permutation problems almost perfectly for a situation that two sources were mixed in a room whose reverberation time was 300 ms.

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Abstract-This paper presents new formulations and algorithms for multichannel extensions of non-negative matrix factorization (NMF). The formulations employ Hermitian positive semidefinite matrices to represent a multichannel version of non-negative elements. Multichannel Euclidean distance and multichannel Itakura-Saito (IS) divergence are defined based on appropriate statistical models utilizing multivariate complex Gaussian distributions. To minimize this distance/divergence, efficient optimization algorithms in the form of multiplicative updates are derived by using properly designed auxiliary functions. Two methods are proposed for clustering NMF bases according to the estimated spatial property. Convolutive blind source separation (BSS) is performed by the multichannel extensions of NMF with the clustering mechanism. Experimental results show that 1) the derived multiplicative update rules exhibited good convergence behavior, and 2) BSS tasks for several music sources with two microphones and three instrumental parts were evaluated successfully.

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This paper proposes a two-stage method for the blind separation of convolutively mixed sources. We employ time-frequency masking, which can be applied even to an underdetermined case where the number of sensors is insuf¿cient for the number of sources. In the ¿rst stage of the method, frequency bin-wise mixtures are classi¿ed based on Gaussian mixture model ¿tting. In the second stage, the permutation ambiguities of the bin-wise classi¿ed signals are aligned by clustering the posterior probability sequences calculated in the ¿rst stage. Experimental results for separating four speeches with three microphones under reverberant conditions show the superiority of the proposed method over existing methods based on time-difference-of-arrival estimations or signal envelope clustering.

We describe the creation and development of a measure that predicts intercultural adjustment potential in Japanese sojourners and immigrants to the US, which we call the ICAPS. We report eight studies that provide evidence for its internal, temporal, and parallel forms reliability; for its predictive ability with not only subjective indices of adjustment, but also with psychometrically standardized measures, peer ratings, and expert ratings; for its convergent validity with a similar measure; for its construct validity with various personality scales; for its incremental validity; and for its external validity in predicting changes as a result of intercultural training, and in identifying experts who work in the intercultural field. We discuss the implications of the availability of this measure to the field for training, research, and education. r