For greater advancement in future communication, efficient noise reduction algorithms with lesser complexity are a necessity. Noise in audio signal poses a great challenge in speech recognition, speech communication, speech enhancement and transmission. Hence the most efficient algorithm for noise reduction must be chosen in such a way that the cost for noise removal is a less as possible, but a large portion of noise is removed. The common method for the removal of noise is optimal linear filtering method, and some algorithms in this method are Wiener filtering, Kalman filtering and spectral subtraction technique. Here, the noise signal is passed through a filter or transformation. However, due to the complexity of these algorithms, there are better algorithms like Signal Dependent Rank Order Mean algorithm (SD-ROM), which removes noise from audio signals and retains the characteristics of the signal. The algorithm can be adjusted depending on the characteristics of noise signal too. To remove white Gaussian noise, discrete wavelet transform technique is used. After each of the techniques are applied to the samples, SNR and elapsed time are calculated. All of the above techniques show an increased Signal to Noise Ratio (SNR) after processing, as seen in the simulation results. © 2017 IEEE.