MATLAB CODING:DIGITAL SIGNAL PROCESSING

3.3 Removing the background noise from the voice signal using the LMS adaptive filter 1. Create voice signal contaminated by the background noise by adding background noise inside signal to voice signal. Create background noise inside by filtering background _noise.wav with the low-pass filter (windows and walls attenuate high frequencies) and shifting back by several samples. Plot this signal and its spectrum and then listen to it. 2. This combined signal will be used as the LMS adaptive filter input x(n). 3. In order to remove background noise from the contaminated voice signal, the background_noise_outside.wav (the original background_noise.wav signal) will be used as the reference input d(n) of the LMS adaptive filter. Run the LMS adaptive filter, plot both the error signal e(n) and its spectrum and listen to it. Calculate signal to noise ratio (SNR) improvement after filtering. Use the SNR equation (1) 4. 3.3 Removing the background noise from the voice signal using the LMS adaptive filter 1. Create voice signal contaminated by the background noise by adding background noise inside signal to voice signal. Create background noise inside by filtering background _noise.wav with the low-pass filter (windows and walls attenuate high frequencies) and shifting back by several samples. Plot this signal and its spectrum and then listen to it. 2. This combined signal will be used as the LMS adaptive filter input x(n). 3. In order to remove background noise from the contaminated voice signal, the background_noise_outside.wav (the original background_noise.wav signal) will be used as the reference input d(n) of the LMS adaptive filter. Run the LMS adaptive filter, plot both the error signal e(n) and its spectrum and listen to it. Calculate signal to noise ratio (SNR) improvement after filtering. Use the SNR equation (1) 4