An adaptive speech enhancement approach based on DCT and empirical mode decomposition

This paper proposed an adaptive hybrid speech enhancement algorithm to reduce the non-stationary noise of a noise contaminated speech. This approach combines the Discrete Cosine Transform (DCT) with an Adaptive Empirical Mode Decomposition (AEMD). The proposed DCT is an extension to the general DCT and the AEMD was developed by adding an extra noise cancellation block for enhancement. The complete approach was carried out in two stages. In the first stage, the noise contaminated speech was enhanced through DCT and adaptive soft thresholding. In second stage, to further reduce the residual noise present in enhanced speech, AEMD was applied through an adaptive noise cancellation block. The performance evaluation of proposed approach was under white and babble noises at various SNR levels.