A Region-Growing Permutation Alignment Approach in Frequency-Domain Blind Source Separation of Speech Mixtures

Abstract

Abstract-The convolutive blind source separation (BSS) problem can be solved efficiently in the frequency domain, where instantaneous BSS is performed separately in each frequency bin. However, the permutation ambiguity in each frequency bin should be resolved so that the separated frequency components from the same source are grouped together. To solve the permutation problem, this paper presents a new alignment method based on an inter-frequency dependence measure: the powers of separated signals. Bin-wise permutation alignment is applied first across all frequency bins, using the correlation of separated signal powers; then the full frequency band is partitioned into small regions based on the bin-wise permutation alignment result. Finally, region-wise permutation alignment is performed in a region-growing manner. The region-wise permutation correction scheme minimizes the spreading of the misalignment at isolated frequency bins to others, hence to improve permutation alignment. Experiment results in simulated and real environments verify the effectiveness of the proposed method. Analysis demonstrates that the proposed frequency-domain BSS method is computationally efficient.

Computational methods for underdetermined convolutive speech localization and separation via model-based sparse component analysis

“…Nesta et al pro-posed an extension for underdetermined scenario where multiple complex valued ICA adaptations jointly estimate the mixing matrix and the temporal activities of multiple sources in each frequency band to exploit the spectral sparsity of speech signals (Nesta and Omologo, 2012). The method does not explicitly rely on identification of the acoustic channel and recovery of the desired source imposes a permutation problem due to mis-alignment of the individual source components (Nesta and Omologo, 2012;Wang et al, 2011). Other extensions of ICA for the underdetermined scenarios consist in integration with sparse masking techniques within a hierarchical separation framework (Araki et al, 2004;Davies and Mitianoudis, 2004).…”

Independent vector analysis using subband and subspace nonlinearity

“…Since ICA in different layers may output the separated results in different order, the permutation ambiguity will occur in FDBSS, which is indicated by the different color of y [f] in Figure 1a. The permutation ambiguity must be carefully addressed by algorithms like [7][8][9][10][11][12] before the inverse STFT is performed, or else the separation procedure will fail.…”

“…in neighboring frequency bins, and this feature is often used to solve the permutation problem [8][9][10]. When the subband technique proposed in the previous section is used, stronger correlations are expected to be observed because of the local dependency property of the data.…”

“…Then, complex-valued ICA algorithms [5,6] are used to separate data in each frequency bin independently. Although FDBSS has many advantages, it suffers from the well-known "permutation problem" [7][8][9][10][11][12]: the separated data must be aligned to make sure that each output signal only contains the data from the same source. After the permutation problem is solved, the inverse STFT is used to reconstruct the sources in the time domain.…”

“…Many algorithms have been proposed in order to overcome the permutation ambiguity in FDBSS. For speech and audio signals, neighboring frequency bins from the same source are strongly correlated, and algorithms like [8][9][10] utilize this feature to correct the permutation problem. In many cases, different signals are likely to come from different directions, so direction of arrival patterns, which are hidden in the demixing matrices, can also be used to solve the permutation problem [10][11][12].…”