TY - JFULL AU - M. Kalamani and S. Valarmathy and M. Krishnamoorthi PY - 2014/7/ TI - Adaptive Noise Reduction Algorithm for Speech Enhancement T2 - International Journal of Electronics and Communication Engineering SP - 1013 EP - 1021 VL - 8 SN - 1307-6892 UR - https://publications.waset.org/pdf/10000110 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 90, 2014 N2 - In this paper, Least Mean Square (LMS) adaptive noise reduction algorithm is proposed to enhance the speech signal from the noisy speech. In this, the speech signal is enhanced by varying the step size as the function of the input signal. Objective and subjective measures are made under various noises for the proposed and existing algorithms. From the experimental results, it is seen that the proposed LMS adaptive noise reduction algorithm reduces Mean square Error (MSE) and Log Spectral Distance (LSD) as compared to that of the earlier methods under various noise conditions with different input SNR levels. In addition, the proposed algorithm increases the Peak Signal to Noise Ratio (PSNR) and Segmental SNR improvement (ΔSNRseg) values; improves the Mean Opinion Score (MOS) as compared to that of the various existing LMS adaptive noise reduction algorithms. From these experimental results, it is observed that the proposed LMS adaptive noise reduction algorithm reduces the speech distortion and residual noise as compared to that of the existing methods. ER -