**AR model of the signal**- Evaluate autoregressive coefficients of the analyzed signal using function
*lpc*(parameters of AR model). Set the order of AR model to*p=16*and evaluate it for signal length*N = 512* - Use short part of voice speech vm0.bin for evaluation purposes

- Evaluate autoregressive coefficients of the analyzed signal using function
**Compare short-time spectra using DFT and LPC approach**- Smoothed estimation of spectrum using LPC is given as frequency responce of sysnthesis filter of AR-model ( function
*freqz*) - Draw both estimation into one figure !!
- Analyse the influence of model gain
*G*to the results ( Gain*G*should be evaluated as the second output parameter of function*lpc*) - Analyse changes in the estimation of smoothed spectrum with respect ot changing order of AR model, i.e.
*p=16, 10, 6*. Explain it !!

- Smoothed estimation of spectrum using LPC is given as frequency responce of sysnthesis filter of AR-model ( function
__Example:__**Compare the spectral resolution for DFT a LPC estimations.**- Choose suitable order for AR model !
- Evaluate spectral estimation for both cases (LPC and DFT) for
*N=64*and*N= 256*. - Use Hamming window weighting.
- sinusoidal:
*f=13 Hz*,*fs= 100 Hz* - 2 sinusoidals of close frequences:
*f1=13 Hz*,*f2=15 Hz*,*fs= 100 Hz*