A Sequential Approach to Sparse Component Analysis

A sequential approach to sparse component analysis (SeqTIF) is proposed in this paper. Although SeqTIF employs the estimation process of the simultaneous TIFROM algorithm, a source cancellation and deflation technique are also incorporated to sequentially estimate speech signals in the mixture. Results indicate that SeqTIF´s separation performance shows a clear improvement upon the simultaneous TIFROM approach, due to the less restrictive assumptions it places upon the signals in the mixture. In particular, the analysis indicates SeqTIF´s data efficiency is high, enabling the sequential approach to track a time-varying mixture with much greater accuracy than the simultaneous algorithm. Furthermore, SeqTIF is a more flexible approach, free from the constraints that a simultaneous approach places upon the mixing system