Speech enhancement based on L₁regularization in the cepstral domain

In this paper, a new speech enhancement algorithm using the L1regularization method in the cepstral domain is proposed. Since voiced speeches have a quasi-periodic nature that allows them to be compactly represented in the cepstral domain, the L1regularization technique can be applied to better control the optimization process required in speech enhancement applications. The proposed algorithm starts with the traditional temporal cepstral smoothing (TCS) method which gives the initial estimation of the power spectrum of the clean speech. It is then refined using a modified L1regularizer which imposes further constraint to the penalty function based on the feature of speech signals in the cepstral domain. A notable improvement of the proposed algorithm over the traditional method is its adaptability to the non-stationary noise. Performance of the proposed algorithm is evaluated using standard measures such as segSNR and PESQ based on a large quantity of speech signals. Our results show that a significant improvement is achieved as compared to the conventional approaches especially in the case that the noise is non-stationary.