A practical speech enhancement system consists of two major components, the estimation of noise power spectrum, and the estimation of speech.In single channel speech enhancement systems, most algorithms require an estimation of average noise spectrum since a secondary channel is not available. This requires a reliable speech/silence detector. Thus the speech/silence detection can be a determini...

This paper describes a series of experiments evaluating the effects of digital processing of speech in noise so as to enhance spectral contrast, using subjects with cochlear hearing loss. The enhancement was carried out on a frequency scale related to the equivalent rectangular bandwidths (ERBs) of auditory filters in normally hearing subjects. The aim was to enhance major spectral prominences ...

The majority of multichannel speech enhancement algorithms are two-step procedures that first apply a linear spatial filter, so-called beamformer, and combine it with single-channel approach for postprocessing. However, the serial concatenation filter postfilter is not generally optimal in minimum mean square error (MMSE) sense noise distributions other than Gaussian distribution. Rather, MMSE ...

In this paper, we propose the modulation-domain Kalman filter (MDKF) for speech enhancement. In contrast to previous modulation domain-enhancement methods based on bandpass filtering, the MDKF is an adaptive and linear MMSE estimator that uses models of the temporal changes of the magnitude spectrum for both speech and noise. Also, because the Kalman filter is a joint magnitude and phase spectr...

Most speech enhancement algorithms are based on the assumption that speech and noise are both Gaussian in the discrete cosine transform (DCT) domain. For further enhancement of noisy speech in the DCT domain, we consider multiple statistical distributions (i.e., Gaussian, Laplacian and Gamma) as a set of candidates to model the noise and speech. We first use the goodness-of-fit (GOF) test in or...