Kostas Kokkinakis

Learn More
In this contribution, we exploit entropy matching to estimate the exponent parameter of a generalized Gaussian density. Based on this premise, we derive a new entropic expression with respect to higher-order moments of the modeled data, which yields a novel generalized source entropy matching estimator (G-EME). A number of other popular statistical methods(More)
Little is known about the extent to which reverberation affects speech intelligibility by cochlear implant (CI) listeners. Experiment 1 assessed CI users' performance using Institute of Electrical and Electronics Engineers (IEEE) sentences corrupted with varying degrees of reverberation. Reverberation times of 0.30, 0.60, 0.80, and 1.0 s were used. Results(More)
In this paper, we evaluate the performance of existing and new objective measures in terms of predicting the quality of reverberant speech and speech enhanced by dereverberation algorithms. We use subjective quality ratings designed to evaluate the quality of speech along three dimensions: speech coloration, reverberation tail effect and overall speech(More)
This paper describes a highly practical blind signal separation (BSS) scheme operating on subband domain data to blindly segregate convolutive mixtures of speech. The proposed method relies on spatiotemporal separation carried out in the time domain by using a multichannel blind deconvolution (MBD) algorithm that enforces separation by entropy maximization(More)
To restore hearing sensation, cochlear implants deliver electrical pulses to the auditory nerve by relying on sophisticated signal processing algorithms that convert acoustic inputs to electrical stimuli. Although individuals fitted with cochlear implants perform well in quiet, in the presence of background noise, the speech intelligibility of cochlear(More)
This paper addresses the blind separation of convolutive and temporally correlated mixtures of speech, through the use of a multichannel blind deconvolution (MBD) method. In the proposed framework (LP-NGA), spatio-temporal separation is carried out by entropy maximization using the well-known natural gradient algorithm (NGA), while a temporal pre-whitening(More)
Bilateral cochlear implants seek to restore the advantages of binaural hearing by improving access to binaural cues. Bilateral implant users are currently fitted with two processors, one in each ear, operating independent of one another. In this work, a different approach to bilateral processing is explored based on blind source separation (BSS) by(More)
The purpose of this study is to determine the relative impact of reverberant self-masking and overlap-masking effects on speech intelligibility by cochlear implant listeners. Sentences were presented in two conditions wherein reverberant consonant segments were replaced with clean consonants, and in another condition wherein reverberant vowel segments were(More)
Bilateral cochlear implant (BI-CI) recipients achieve high word recognition scores in quiet listening conditions. Still, there is a substantial drop in speech recognition performance when there is reverberation and more than one interferers. BI-CI users utilize information from just two directional microphones placed on opposite sides of the head in a(More)
In this paper we propose a general method for separating mixtures of multiple audio signals observed in a real acoustic environment. The multipath nature of acoustic propagation is addressed by the use of the FIR polynomial matrix algebra, while spatio-temporal separation is achieved by entropy maximization using the natural gradient algorithm. The(More)