Monaural Speech Enhancement Using Joint Dictionary Learning with Cross-Coherence Penalties

In the real world, the interferers are often nonstationary and potentially similar to the speech where the conventional speech enhancement (SE) approaches are often incompetent. In the recently proposed sparsity-based approaches, the clean speech is often recovered from the degraded speech by sparse coding of the mixture over the composite dictionary consisting of the speech and interferer dictionaries. However, parts of the speech component are explained by interferer dictionary atoms and vice-versa, which cause source confusion. The existing approaches learn the speech and interferer dictionaries separately and the source confusion is relatively large. In this paper, we introduce a new joint dictionary learning (JDL) method for SE which learns the speech and interferer dictionaries jointly. In the proposed method, the information of speech, interferer and their mixture and the cross-coherence of dictionaries are taken into account. These two parts constitute the new cost function and an algorithm is presented to solve this JDL optimization problem. The experimental results show that our proposed approach can obtain better performances than other tested approaches, and the advantages are more obvious when the input signal-to-interferer ratios are low.