2. (10 marks) In this exercise, we will apply AR modeling to speech samples. Download m(lae.wav, "wolae.wav, wolih.wav, and wOluw.wav from Resources -> MATLAB directory. Complete the following: a. For each speech sample, plot the estimated variance of the white noise input against the model order, with the model order ranging from 1 to 25. See the documentation for aryule command for accessing the estimated variance. Comment on the results. What would be a good model order for modelling these waveforms? b. For each speech sample and the chosen model order, compute and plot the periodogram and AR spectral estimates. See "LinearPredictionExample.mlx for guidance. Comment on the results. In particular, what is the AR spectral estimate trying to model? Are the AR spectral estimates the same across the four speech samples? 2. (10 marks) In this exercise, we will apply AR modeling to speech samples. Download m(lae.wav, "wolae.wav, wolih.wav, and wOluw.wav from Resources -> MATLAB directory. Complete the following: a. For each speech sample, plot the estimated variance of the white noise input against the model order, with the model order ranging from 1 to 25. See the documentation for aryule command for accessing the estimated variance. Comment on the results. What would be a good model order for modelling these waveforms? b. For each speech sample and the chosen model order, compute and plot the periodogram and AR spectral estimates. See "LinearPredictionExample.mlx for guidance. Comment on the results. In particular, what is the AR spectral estimate trying to model? Are the AR spectral estimates the same across the four speech samples