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In this paper we propose to extend a recently introduced clustering approach for solving the permutation ambiguity in convolutive blind source separation to a case where spatial aliasing occurs. A well known approach for separation of sources is the transformation to the time-frequency domain, where the task can be reduced to multiple instantaneous problems. While these may be easily solved using...
In this paper we propose a new clustering approach for solving the permutation ambiguity in convolutive blind source separation. After the transformation to the time-frequency domain, the problem of separation of sources can be reduced to multiple instantaneous problems, which may be solved using independent component analysis. The drawbacks of this approach are the inherent permutation and scaling...
In this paper, we propose a modification to dyadic sorting scheme used for the permutation problemin convolutive blind source separation. In the frequency domain, the problem of separation of sources can be reduced to multiple instantaneous problems, which are easily solvable using independent component analysis. However, this simplified method leads to the problem of correctly aligning and scaling...
This paper presents a new algorithm for addressing the permutation ambiguity in convolutive blind source separation. The proposed algorithm seeks to prevent permutations by frequency oversampling, and then exploiting the induced correlation between bins. Any remaining permutation is then corrected by beam pattern estimation. Cascade initialization is shown to improve system performance while decreasing...
This paper compares the performance of several blind source separation (BSS) algorithms in environments of varying reverberation, noise, microphone spacing, and sparsity. Of particular interest are two frequency domain algorithms; one Cascaded ICA with Intervention Alignment (CICAIA), and one algorithm by Pham, Servière, and Boumaraf. The former is found to work exceptionally well in high noise, low...
In this paper, we present a new algorithm for solving the permutation ambiguity in convolutive blind source separation. A common approach for separation of convolutive mixtures is the transformation to the time-frequency domain, where the convolution becomes a multiplication. This allows for the use of well-known instantaneous ICA algorithms independently in each frequency bin. However, this simplication...
In this paper, we propose to use the scaling ambiguity of convolutive blind source separation for shortening the unmixing filters. An often used approach for separating convolutive mixtures is the transformation to the time-frequency domain where an instantaneous ICA algorithm can be applied for each frequency separately. This approach leads to the so called permutation and scaling ambiguity. While...
In this paper, we present a new algorithm for solving the permutation ambiguity in convolutive blind source separation. Transformed to the frequency domain, existing algorithms can efficiently solve the reduction of the source separation problem into independent instantaneous separation in each frequency bin. However, this independency leads to the problem of correctly aligning these single bins....
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