Bidirectional EMD-RLS: Performance analysis for denoising in speech signal

Empirical Mode Decomposition (EMD) is an alternative decomposition method that allows the analysis of non-stationary and non-linear signals, decomposing them into components called Intrinsic Mode Functions (IMFs). In this paper, we introduce the bidirectional combination between EMD and the Recursive Least Squares (RLS) adaptive filter and compare it to Least Mean Square (LMS), EMD-LMS and RLS methods. The hybrid algorithm relies on the determination of vertical (IMFs indexes) and horizontal (temporal indexes) weighting coefficients. We evaluated the effectiveness of the methods by filtering sets of speech signals artificially corrupted by white Gaussian noise. To find the method that provides the greatest noise reduction and the best speech quality, we took into account the Signal-to-Noise Ratio and the Perceptual Evaluation of Speech Quality (PESQ) score. The analyzed cases showed that EMD-RLS can provide results as good as the other methods, but with a shorter learning curve than LMS and EMD-LMS, despite the higher computational cost. For a single training iteration, the methods presented PESQ scores on average equivalent to each other. However, they showed a significant difference in performance regarding noise reduction, with the best result obtained by RLS, followed by EMD-RLS, EMD-LMS and LMS.