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Speech Enhancement Based On Noise Reduction

This paper addresses the problem of signal distortion often faced in real life scenarios like teleconferencing, VoIP, cellular calls, speech recognition due to presence of unwanted background noise. This acoustic noise gets automatically added to the signal and is picked up by microphone causing a reduction in the perceived quality or intelligibility of the audio signal at the receiver end. Consequently the techniques for enhancing speech degraded by uncorrelated additive noise, when only noisy speech is available, have been widely studied in the past and are still an active field of research. Some of the feasible solutions include implementation of certain speech enhancement algorithms at the receiver side to enhance perceived sound quality or by using hearing aids which have built in noise reduction hardware. In this paper, we present various adaptive algorithms developed for noise cancellation in past few years namely LMS (Least Mean Square), NLMS (Normalized Least Mean Square) and RLS (Recursive Least Square) and LPC (Linear Predictive Coding). We then compare the noise cancellation performance of these algorithms in terms of percentage of noise removal in the restored signal. The simulation results on MATLAB confirmed that RLS algorithm leads to best subjective results and objective results but at the cost of large computational complexity and memory requirement.