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This paper describes the frequency-domain approach to the blind source separation of speech/audio signals that are convolutively mixed in a real room environment. With the application of short- time Fourier transforms, convolutive mixtures in the time domain can be approximated as multiple instantaneous mixtures in the frequency domain. We employ(More)
This paper presents a prototype system for blind source separation (BSS) of many speech signals and describes the techniques used in the system. Our system uses 8 microphones located at the vertexes of a 4 cm times 4 cm times 4 cm cube and has the ability to separate signals distributed in three-dimensional space. The mixed signals observed by the(More)
A maximum a-posteriori approach for overcomplete blind source separation based on Laplacian priors usually involves /spl lscr//sub 1/-norm minimization. It requires different approaches for real and complex numbers as they appear for example in the frequency domain. In this paper we compare a combinatorial approach for real numbers with a second order cone(More)
The present work proposes a self tuning PID regulatory control method based on generalized minimum variance evaluation. The proposed one is categorized by implicit self-tuning control which directly tunes the PID gains without identifying the plant model. Most of implicit self-tuning control approach updates control parameters by evaluating the tracking(More)
This paper derives two spatio-temporal extensions of the well-known FastICA algorithm of Hyvarinen and Oja that are applicable to the convolutive blind source separation task. Our time-domain algorithms combine multichannel spatio-temporal prewhitening via multistage least-squares linear prediction with novel adaptive procedures that impose paraunitary(More)
Blind source separation (BSS) problem consists of estimating N sources from M mixtures without using source and mixing information. In this paper we focus on improving BSS method called time-frequency binary masking (TFBM). TFBM is a versatile approach due to its ability to separate signals in both (over-)determined case (N M ≤) and underdetermined case (N(More)
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