Speech enhancement based on Sparse Code Shrinkage employing multiple speech models

This paper presents a single-channel speech enhancement system based on the Sparse Code Shrinkage (SCS) algorithm and employment of multiple speech models. The enhancement system consists of two stages: training and enhancement. In the training stage, the Gaussian mixture modelling (GMM) is employed to cluster speech signals in ICA-based transform domain into several categories, and for each category a super-Gaussian model is estimated that is used during the enhancement stage. In the enhancement stage, the estimate of each signal frame is obtained as a weighted average of estimates obtained by using each speech category model. The weights are calculated according to the probability of each category, given the signal enhanced using the conventional SCS algorithm. During the enhancement, the individual speech category models are further adapted at each signal frame. Experimental evaluations are performed on speech signals from the TIMIT database, corrupted by Gaussian noise and three real-world noises, Subway, Street, and Railway noise, from the NOISEX-92 database. Evaluations are performed in terms of segmental SNR, spectral distortion and PESQ measure. Experimental results show that the proposed multi-model SCS enhancement algorithm significantly outperforms the conventional WF, SCS and multi-model WF algorithms.

► Single-channel speech enhancement in ICA transform domain. ► Sparse Code Shrinkage and employment of multiple non-Gaussian models of speech. ► Gaussian mixture modelling used to cluster speech signals. ► Experiments on TIMIT data corrupted by Gaussian and real-world noises. ► Substantial improvements over Sparse Code Shrinkage and multi-model Wiener filter.