A speech enhancement approach based on noise classification

摘要

For speech enhancement, most existing approaches do not consider the differences, between various types of noise, which significantly affect the performance of speech enhancement. In this paper, we propose a novel speech enhancement approach by taking into account the different characteristic statistical properties of various noise on the basis of noise classification. To classify noise, an effective noise classification method is firstly developed by exploiting the features of noise energy distribution in the Bark domain. Then, based on the noise types, the speech enhancement approach is obtained by forming the optimal parameter combinations for the optimally modified log-spectral amplitude (OM-LSA) speech estimator with the improved minima controlled recursive averaging (IMCRA) noise estimator, where the parameter combinations consisting of the smoothing parameters for smoothing the noisy power spectrum and the recursive averaging in the noise spectrum estimation as well as the weighting factor for the a priori SNR estimation, are built through the enhancement of noisy speech samples. Finally, extensive experiments are carried out in terms of objective evaluation under various noise conditions, and the experimental results show that the proposed approach yields better performance compared with the conventional OM-LSA with IMCRA in speech enhancement. (C) 2015 Elsevier Ltd. All rights reserved.