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.
['Alexander Schasse', 'Rainer Martin', 'Ulrich Kornagel', 'Eghart Fischer', 'Henning Puder']
Efficient estimation of inter-subband speech correlations