| Paper: | SP-L10.1 | ||
| Session: | Multichannel Speech Enhancement | ||
| Time: | Friday, May 21, 13:00 - 13:20 | ||
| Presentation: | Lecture | ||
| Topic: | Speech Processing: Speech Enhancement | ||
| Title: | OPTIMAL BLIND SEPARATION OF CONVOLUTIVE AUDIO MIXTURES WITHOUT TEMPORAL CONSTRAINTS | ||
| Authors: | Kostas Kokkinakis; University of Liverpool | ||
| Asoke K. Nandi; University of Liverpool | |||
| Abstract: | This paper addresses the blind separation of convolutive and temporally correlated speech mixtures, through the use of a multichannel blind deconvolution (MBD) method. In the proposed method (NGA-LP) spatio-temporal separation is achieved by entropy maximization using the natural gradient algorithm (NGA), while a temporal prewhitening stage, based on linear prediction (LP), preserves the original spectral characteristics of each source contribution. It is further shown that a parameterized optimal nonlinearity derived from the generalized Gaussian density (GGD) model, increases the overall separation performance. Experiments with convolutive mixtures illustrate the merits of the proposed method. | ||
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