Performance Analysis of LMS, NLMS Adaptive Algorithms for Speech Enhancement in Noisy Environment
Ch. D. Umasankar1, M Satya Sairam2
1Ch. D. Umasankar*, Research Scholar, Dept. of ECE, Dr. Y S R University College of Engineering, Acharya Nagarjuna University, Guntur.
2Dr. M. Satya Sairam, Dept. of ECE, RVR&JC College of Engineering, Guntur.
Manuscript received on January 17, 2020. | Revised Manuscript received on January 24, 2020. | Manuscript published on February 10, 2020. | PP: 2330-2333 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1864029420/2020©BEIESP | DOI: 10.35940/ijitee.D1864.029420
Open Access | Ethics and Policies | Cite | Mendeley
© 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: The speech enhancement is an important technique to remove noise from corrupted speech signal. Here several Adaptive Algorithms were proposed to improve quality of speech signal. In this paper to estimate the speech enhancement performance with variety of noise reduction algorithms using adaptive filters like LMS, NLMS. Simulations were performed on noisy data which was prepared by adding machinegun, Factory, vehicle and Traffic noise at 0dB, 5dB and 10dB SNR levels to clean speech samples The performance comparison of adaptive noise cancellation (ANC) system using LMS and NLMS algorithms was carried by means of signal to noise ratio (SNR), mean square error (MSE) and root mean square error (RMSE). Based on performance analysis, the NLMS algorithm was found to be a better optimal adaptive noise canceller for speech signal
Keywords: Adaptive Filtering, Adaptive Noise Cancellation (ANC), LMS (Least Mean Squares), NLMS (Normalized LMS), SNR, MSE, RMSE.
Scope of the Article: Environmental Engineering