Enhancing Noisy Speech using WEMD
J.V. Thomas Abraham1, A. Shahina2, A. Nayeemulla Khan3

1J.V. Thomas Abraham, School of Computing Science and Engineering, VIT University Chennai Campus, Chennai (Tamil Nadu), India.
2A. Shahina, Department of Information Technology, SSN College of Engineering, Kalavakkam, Chennai (Tamil Nadu), India.
3A. Nayeemulla Khan, School of Computing Science and Engineering, VIT University Chennai Campus, Chennai (Tamil Nadu), India.
Manuscript received on 29 April 2019 | Revised Manuscript received on 11 May 2019 | Manuscript Published on 17 May 2019 | PP: 644-647 | Volume-7 Issue-6S4 April 2019 | Retrieval Number: F11320476S419/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Speech signal distortion is unavoidable in real time applications. This distorted signal can adversely affect the performance of systems based on speech signals. Automatic speaker recognition (ASR) system performs well with clean speech signals while its performance degrades drastically with noisy speech. Enhancing the speech signal aims at improving the quality of the speech signal by reducing the noise contamination, thereby improving the performance of the ASR system. Noise could be background noise, reverberation, babble noise etc. In this paper, to improve the distorted speech signal, we propose a two stage speech enhancement algorithm where Empirical Mode Decomposition (EMD) with adaptive threshold in IMF selection is done at the first stage and then employ wavelet denoising (WD) in the second stage. The two stage denoising method is used to reduce noise in high and low frequencies. The effectiveness of the proposed algorithm is compared with a few baseline algorithms used for enhancement.
Keywords: Speech Enhancement, Empirical Mode Decomposition, Wavelet Denoising.
Scope of the Article: Vision and Speech Perception