Online IVA with Adaptive Learning for Speech Separation Using Various Source Priors

摘要

Independent vector analysis (IVA) is a frequency domain blind source separation (FDBSS) technique that has proven efficient in separating independent speech signals from their convolutive mixtures. In particular, it addresses the problematic permutation problem by using a multivariate source prior. The multivariate source prior models statistical inter dependency across the frequency bins of each source and the performance of the method is dependent upon the choice of source prior. The online form of the IVA is suitable for practical real time systems. Previous online algorithms use a learning rate that does not introduce a robust way to control the learning as a function of the proximity to the target solution. In this work, we propose a new adaptive learning scheme to improve the convergence speed and steady state separation performance. The speech signals are modelled by two different source priors; a super-Gaussian distribution and a generalized Gaussian distribution. The experimental results confirm improved performance with real room impulse responses and real recorded speech signals.