NDLI: Independent vector analysis based on overlapped cliques of variable width for frequency-domain blind signal separation

Content Provider	Springer Nature : SpringerOpen
Author	Lee, Intae Jang, Gil-Jin
Abstract	A novel method is proposed to improve the performance of independent vector analysis (IVA) for blind signal separation of acoustic mixtures. IVA is a frequency-domain approach that successfully resolves the well-known permutation problem by applying a spherical dependency model to all pairs of frequency bins. The dependency model of IVA is equivalent to a single clique in an undirected graph; a clique in graph theory is defined as a subset of vertices in which any pair of vertices is connected by an undirected edge. Therefore, IVA imposes the same amount of statistical dependency on every pair of frequency bins, which may not match the characteristics of real-world signals. The proposed method allows variable amounts of statistical dependencies according to the correlation coefficients observed in real acoustic signals and, hence, enables more accurate modeling of statistical dependencies. A number of cliques constitutes the new dependency graph so that neighboring frequency bins are assigned to the same clique, while distant bins are assigned to different cliques. The permutation ambiguity is resolved by overlapped frequency bins between neighboring cliques. For speech signals, we observed especially strong correlations across neighboring frequency bins and a decrease in these correlations with an increase in the distance between bins. The clique sizes are either fixed, or determined by the reciprocal of the mel-frequency scale to impose a wider dependency on low-frequency components. Experimental results showed improved performances over conventional IVA. The signal-to-interference ratio improved from 15.5 to 18.8 dB on average for seven different source locations. When we varied the clique sizes according to the observed correlations, the stability of the proposed method increased with a large number of cliques.
Related Links	https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/1687-6180-2012-113
Ending Page	12
Page Count	12
Starting Page	1
ISSN	16876180
DOI	10.1186/1687-6180-2012-113
Journal	EURASIP Journal on Advances in Signal Processing
Issue Number	1
Volume Number	2012
Language	English
Publisher	SpringerOpen
Publisher Date	2012-05-23
Access Restriction	Open
Subject Keyword	blind signal separation (BSS) independent component analysis (ICA) independent vector analysis (IVA)
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Signal Processing Hardware and Architecture
Aim	The aim of the EURASIP Journal on Advances in Signal Processing is to highlight the theoretical and practical aspects of signal processing in new and emerging technologies. The journal is directed as much at the practicing engineer as at the academic researcher. Authors of articles with novel contributions to the theory and/or practice of signal processing are welcome to submit their articles for consideration. All manuscripts undergo a rigorous review process. EURASIP Journal on Advances in Signal Processing employs a paperless, electronic review process to enable a fast and speedy turnaround in the review process.The journal is an Open Access journal since 2007.
Journal Impact Factor	1.9/2024
5-Year Journal Impact Factor	2.0/2024

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