A TWO CHANNEL BLOCK-ADAPTIVE AUDIO SEPARATION TECHNIQUE BASED UPON TIME-FREQUENCY INFORMATION (TuePmOR4)

Author(s) : Daniel Smith (SECTE, University of Wollongong, Australia) Jason Lukasiak (SECTE, University of Wollongong, Australia) Ian Burnett (SECTE, University of Wollongong, Australia)

Abstract : TIFROM 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. 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 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.

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