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.
Highlights
When an audio signal is recorded by a microphone in a closed room, it reaches the microphone via a direct path, and infinitely many reverberant paths
5 Conclusions The totally spherical dependency model of independent vector analysis (IVA) was relaxed by the dependency models of chained cliques
The new clique designs are advantageous because the weak dependency among distant frequencies is modeled by indirect dependency propagation, which helps in finding a better local solution compared to the original IVA, where the same amount of dependency is assigned to any pair of frequency bins
Summary
When an audio signal is recorded by a microphone in a closed room, it reaches the microphone via a direct path, and infinitely many reverberant paths. When compared to the frequency-domain ICA followed by perfect permutation correction, the separation performance of IVA using spherically symmetric joint densities is slightly inferior [19]. This suggests that such source priors do not exactly match the distribution of speech signals and that the IVA performance for speech separation can be improved by finding better dependency models [22,23]. To derive the objective function of IVA, a single dimension of the estimated sources in Equation 4 is extracted, and a new vector is constructed by collecting the source coefficients of all the frequency bins.
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