Independent vector analysis using subband and subspace nonlinearity

Abstract

Independent vector analysis (IVA) is a recently proposed technique, an application of which is to solve the frequency domain blind source separation problem. Compared with the traditional complex-valued independent component analysis plus permutation correction approach, the largest advantage of IVA is that the permutation problem is directly addressed by IVA rather than resorting to the use of an ad hoc permutation resolving algorithm after a separation of the sources in multiple frequency bands. In this article, two updates for IVA are presented. First, a novel subband construction method is introduced, IVA will be conducted in subbands from high frequency to low frequency rather than in the full frequency band, the fact that the inter-frequency dependencies in subbands are stronger allows a more efficient approach to the permutation problem. Second, to improve robustness and against noise, the IVA nonlinearity is calculated only in the signal subspace, which is defined by the eigenvector associated with the largest eigenvalue of the signal correlation matrix. Different experiments were carried out on a software suite developed by us, and dramatic performance improvements were observed using the proposed methods. Lastly, as an example of real-world application, IVA with the proposed updates was used to separate vibration components from high-speed train noise data.

Adaptive independent vector analysis for multi-subject complex-valued fMRI data

“…As such, the nonlinearity (| | ) can be learned within the 1-dimensional subspace spanned by the dominant eigenvector (Na et al, 2013), resulting in a de-noised nonlinear function. Denoting the dominant eigenvalue as and the eigenvector , -, | | in the dominant subspace is expressed as ( | |) .…”

“…Furthermore, to decrease the noise effect, we adopted a subspace de-noising strategy (Na et al, 2013), and updated the MGGD-based nonlinearity in the dominant SCV subspace. Furthermore, we utilized a post-IVA phase de-noising strategy to remove noisy voxels from the SM estimates.…”

A hybrid algorithm for blind source separation of a convolutive mixture of three speech sources

“…Most of the algorithms only use simulated room environments for mixing matrix instead of real room as shown in [22,23]. Apart from this, the temporal whitening caused by the equalisation filters w 11 , w 22 and w 33 will render the output useless.…”

IVA using complex multivariate GGD: application to fMRI analysis

“…Second, for each source, the covariance matrix is estimated using its dominant eigenvalue only. This is informally justified based on experimental observations in multiband frequency-domain acoustic data in (Na et al, 2013) but justification for other sources is unclear.…”