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Audio video based fast fixed-point independent vector analysis for multisource separation in a room environment

Yanfeng Liang*, Syed Mohsen Naqvi and Jonathon A Chambers

Author Affiliations

School of Electronic, Electrical and System Engineering, Loughborough University, Leicestershire, LE11 3TU, UK

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EURASIP Journal on Advances in Signal Processing 2012, 2012:183  doi:10.1186/1687-6180-2012-183

Published: 22 August 2012


Fast fixed-point independent vector analysis (FastIVA) is an improved independent vector analysis (IVA) method, which can achieve faster and better separation performance than original IVA. As an example IVA method, it is designed to solve the permutation problem in frequency domain independent component analysis by retaining the higher order statistical dependency between frequencies during learning. However, the performance of all IVA methods is limited due to the dimensionality of the parameter space commonly encountered in practical frequency-domain source separation problems and the spherical symmetry assumed with the source model. In this article, a particular permutation problem encountered in using the FastIVA algorithm is highlighted, namely the block permutation problem. Therefore a new audio video based fast fixed-point independent vector analysis algorithm is proposed, which uses video information to provide a smart initialization for the optimization problem. The method cannot only avoid the ill convergence resulting from the block permutation problem but also improve the separation performance even in noisy and high reverberant environments. Different multisource datasets including the real audio video corpus AV16.3 are used to verify the proposed method. For the evaluation of the separation performance on real room recordings, a new pitch based evaluation criterion is also proposed.