<![CDATA[The Empirical Mode Decomposition is raising significant interest since its first introduction among the nineties. The attention in varied fields such as medical engineering, space analysis, hydrology, synthetic aperture measuring, speech enhancement, watermarking and etc. Hurst exponent statistics was adopted for identifying and selecting the set of Intrinsic Mode Functions (IMF) that are most affected by the noise components. Moreover, the speech signal was reconstructed by subsequently the least degraded IMF. Hereafter, in this article, SWEMD method is enhanced by using Sliding Window (SW) procedure. This research work has come SDG goals for health and well-being and also this research work concentrated on hearing aid application using noise level adjustment. In this SWEMDH method, the calculation of EMD is performed based on the small and sliding window along with the time axis. For each component, the total of sifting iterations is unwavering by decomposition of many signal windows by standard algorithm and calculating the average amount of sifting steps for each component. The median filter used for removed nonlinear components of this work. SWEMDH technique removed for low frequency Noisy Components. The speech quality was evaluation by the performance matrices of Mean Square Error, Perceptual evaluation of speech quality, signal to noise ratio, peak signal to noise ratio. Finally, the experimental results show the considerable improvements in speech enhancement under non-stationary noise environments.]]>
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