Efficient estimation of inter-subband speech correlations
Alexander Schasse, Rainer Martin, Ulrich Kornagel, Eghart Fischer, Henning Puder
Abstract
We propose an approach to compute the inter-subband correlation (ISBC) of noisy speech signals to distinguish between speech and noise segments in the time-frequency plane. The proposed spectral correlation estimator provides information about the input signal which can be used to derive a binary mask or the speech-presence probability. Unlike other approaches it does not require an estimate of the noise power. To this end we analyse a received noisy speech signal in the modulation domain and identify similarly modulated subband signals within a range of modulation frequencies that are typical for speech signals. Based on this pre-processing step, we identify a single reference subband that most likely contains speech and estimate the spectral correlations with respect to this reference band. The algorithm proposed in this paper aims at a very low computational complexity which makes it suitable for hearing aids.
BibTeX
@inproceedings{icassp2016_efficientestimat,
title = {Efficient estimation of inter-subband speech correlations},
author = {Alexander Schasse and Rainer Martin and Ulrich Kornagel and Eghart Fischer and Henning Puder},
booktitle = {ICASSP 2016},
year = {2016}
}