Online inter-frame correlation estimation methods for speech enhancement in frequency subbands

In this paper, we propose solutions for the online adaptation of optimal FIR filters for speech enhancement in DFT subbands. An important ingredient to such filters is the estimation of the inter-frame correlation of the clean speech signal. While this correlation was assumed to be perfectly known in former studies, we discuss two online estimation approaches based on a constant noise inter-frame correlation and on the use of a binary mask. We show that a filtering of subband signals based on these estimated quantities outperforms a conventional, instantaneous spectral weighting, such as the frequency-domain Wiener filter at least for high SNR conditions.