A negentropy based adaptive line enhancer for single-channel noise reduction at low SNR conditions

In this paper, we propose an adaptive line enhancer based on negentropy for single-channel noise reduction. Our proposed approach can be integrated in a speech enhancement system as a preprocessor to be combined with other noise reduction approaches. The proposed method performs the noise reduction by splitting the noisy speech components into the deterministic and the stochastic parts through the minimization of negentropy in an adaptive manner. We consider the negentropy as a cost function, and we derive a learning rule via Newton´s method to minimize the negentropy of the error signal. By the experimental results, we demonstrate that exploiting the proposed approach can be potentially useful as a preprocessor for improving the performance of conventional single-channel noise reduction approaches at low signal-to-noise ratio (SNR) conditions. Moreover, it is shown that our approach by itself can also enhance the noisy speech in an adverse noisy environment.