Hiroshi Sawada

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Blind source separation (BSS) for convolutive mixtures can be solved efficiently in the frequency domain, where independent component analysis (ICA) is performed separately in each frequency bin. However, frequency-domain BSS involves a permutation problem: the permutation ambiguity of ICA in each frequency bin should be aligned so that a separated signal(More)
This paper presents a new method for blind sparse source separation. Some sparse source separation methods, which rely on source sparseness and an anechoic mixing model, have already been proposed. These methods utilize level ratios and phase differences between sensor observations as their features, and they separate signals by classifying them. However,(More)
This paper presents a blind source separation method for convolutive mixtures of speech/audio sources. The method can even be applied to an underdetermined case where there are fewer microphones than sources. The separation operation is performed in the frequency domain and consists of two stages. In the first stage, frequency-domain mixture samples are(More)
This paper presents a new method for grouping bin-wise separated signals for individual sources, i.e., solving the permutation problem, in the process of frequency-domain blind source separation. Conventionally, the correlation coefficient of separated signal envelopes is calculated to judge whether or not the separated signals originate from the same(More)
This paper proposes a new formulation and optimization procedure for grouping frequency components in frequency-domain blind source separation (BSS). We adopt two separation techniques, independent component analysis (ICA) and time-frequency (T-F) masking, for the frequency-domain BSS. With ICA, grouping the frequency components corresponds to aligning the(More)
This article provides an overview of the first stereo audio source separation evaluation campaign, organized by the authors. Fifteen underdetermined stereo source separation algorithms have been applied to various audio data, including instantaneous, convolutive and real mixtures of speech or music sources. The data and the algorithms are presented and the(More)
This paper presents a new method to express functional permissibilities for look-up table (LUT) based field programmable gate arrays (FPGAs). The method represents functional permissibilities by using sets of pairs of functions, not by incompletely specified functions. It makes good use of the properties of LUTs such that their internal logics can be freely(More)
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 insufficient for the number of sources. In the first stage of the method, frequency bin-wise mixtures are classified based on Gaussian mixture(More)
We address the problem of underdetermined BSS. While most previous approaches are designed for instantaneous mixtures, we propose a time-frequency-domain algorithm for convolutive mixtures. We adopt a two-step method based on a general maximum a posteriori (MAP) approach. In the first step, we estimate the mixing matrix based on hierarchical clustering,(More)
We ropose in this paper a new exact algorithm and gr aduay improvement methods of minimizing binary decision diagrams (BDD’s). In the exact minimization algorithm the o timum order is searched by the exchanges o! variabyes of BDD’s based on the framework of Friedman’s algorithm. The use of BDD representation of a given function and intermediate functions(More)