A two channel, block-adaptive audio separation technique based upon time-frequency information

TIFROM [1, 2] is a two channel separation technique, which is well suited to separating audio signals, and in particular, dependent signals that fall outside the scope of conventional BSS applications [1]. One problem with TIFROM however, is degraded performance due to inconsistent estimation of the mixing system. To reduce these inconsistencies, we present a modified algorithm that incorporates k-means clustering [3] and normalised variance, improving upon TIFROM estimation results significantly. To improve TIFROM data efficiency we also include a weighting (running average) function for mixing column estimates. This transforms our modified algorithm into a block based adaptive algorithm with the ability to track a slowly time-varying mixture.