This paper intends to provide the optimum method between the Wiener filtering method and the Neural networks method for speech signal enhancement. A speech signal is highly susceptible to various noises. Many denoising methods include removal of high frequency components from the original signal. But this leads to removal of few parts of original signal. Thus, the quality of the signal reduces which is highly undesirable. Our main objective is to denoise the signal while we enhance its quality. Two methods, namely fully connected and convolutional neural network methods are compared with the Wiener filtering method and the most suitable technique will be suggested. To compare the output signal quality, we compute Signal to Noise ratio (SNR) and Peak Signal to Noise Ratio (PSNR). An advanced version of MATLAB with the advanced toolboxes such as Deep Learning toolbox, Audio toolbox, Signal Processing toolbox etc will be utilized for speech denoising and its quality enhancement.

We have compared two Neural network models with Wiener filtering technique for Speech signal enhancement. Our paper intends to suggest the best method suitable for speech denoising and quality enhancement. We have utilized MATLAB software with most advanced toolboxes for building the models. For comparing our models, we computed PSNR and SNR values.

We have compared two Neural network models with Wiener filtering technique for Speech signal enhancement. Our paper intends to suggest the best method suitable for speech denoising and quality enhancement. We have utilized MATLAB software with most advanced toolboxes for building the models. For comparing our models, we computed PSNR and SNR values.