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In Source Separation research, "cocktail party problem" is a challenging problem that research into source separation aims to solve. Many attempts have been made to solve this complex problem. A logical approach would be to break down this complex problem into several smaller problems which are solved in different stages — each considering various aspects. In this paper, we are providing...
In this paper, minimization of the statistical dependence is exploited for acoustic source localization purposes. Originally developed for the separation of signal mixtures, we show that Independent Component Analysis (ICA) can also be successfully applied to localize multiple simultaneously active sound sources, with possibly less sensors than sources. First, the recently proposed Averaged Directivity...
The independent component analysis (ICA) is a commonly used method to find the demixing matrix for the blind source separation (BSS). For speech signals, we should solve BSS problems in the convolutive mixing model, i.e., ICA technique is extended to the frequency domain. The cross-spectral density matrices are computed for each frequency bin instead of covariance matrices in time domain. The joint...
Given a time series of multicomponent measurements x(t), the usual objective of nonlinear blind source separation (BSS) is to find a ??source?? time series s(t), comprised of statistically independent combinations of the measured components. In this paper, the source time series is required to have a density function in (s, \mathdot s)-space that is equal to the product of density functions of individual...
Wavelet packets decompose signals in to broader components using linear spectral bisecting. Mixing matrix is the key issue in the Blind Source Separation (BSS) literature especially in under-determined cases. In this paper, we propose a simple and novel method in short time wavelet packet analysis to estimate blindly the mixing matrix of speech signals from noise free linear mixtures in over-complete...
The performance of the blind signal separation (BSS) must be evaluated using not only quality of separated signals but also absolute level of residual noise signals (crosstalk components). The purpose of our study is to suppress the recognizable crosstalk components and to extract only the target speech using a post-processing unit. In this paper, the proposed methods are analyzed in detail. Additionally,...
This paper presents a novel technique for blind source separation (BSS) of anechoic speech mixtures in the underdetermined case. A demising algorithm that exploits the sparsity of the short time Fourier transform (STFT) of speech signals is proposed. The algorithm merges constrained optimization with ideas based on the degenerate unmixing estimation technique (DUET) (O. Yilmaz and S. Rickard, 2004)...
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