: Audio Processing & Digital Equalization

1. Load and plot the ICP signal (fs=125 Hz). Design an FIR lowpass filter to filter out the

high-frequency noise due to quantization (i.e., smooth the signal but without changing

its key properties) and apply it to the signal (using filtfilt). Plot and compare the initial

signal and the filtered signal. Discuss how you decided what frequencies to cut and the

results.

2. Design an FIR high-pass filter to eliminate the low-frequency trend (i.e. the DC value

of the output icp signal should be 0), but keeping all the high-frequency content (i.e.,

the pulsatile nature of the signal intact). Apply the filter to the ICP signal (filtfilt), plot

and compare the initial signal and the filtered signal. Discuss how you decided what

frequencies to cut and the results.

3. Design an FIR banpass filter to eliminate low-frequency trend but keeping all the high-

frequency content (i.e., the pulsatile nature of the signal intact) except for the distortion

due to quantization. Apply the filter to the ICP signal, plot and compare the initial

signal and the filtered signal. Discuss how you decided what frequencies to cut and the

results.

4. Design an FIR bandpass filter to eliminate the low frequency trend (i.e. the DC value

of the output icp signal should be 0), and eliminate the high-frequency content except

for the fundamental harmonic due to the cardiac component (i.e., the output should be

close to a sinusoidal signal for each heart beat). Apply the filter to the ICP signal, plot

and compare the initial signal and the filtered signal. Discuss how you decided what

frequencies to cut and the results.

5. Load and plot the signal ECGNoisy60Hz in MATLAB. Notice the noise present at 60

Hz. Use MATLAB to design a filter to eliminate this problem. Plot and compare the

initial signal and the filtered signal.

6. Repeat the procedure with ECGQuantization. In this case the signal is severely affected

by quantization noise. Use MATLAB to design a filter to eliminate this noise and show

the results.

7. Repeat the procedure with ECGBaselineDrift. In this case the signal is severely affected

by baseline drift due to patient movement. Use MATLAB to design a filter to eliminate

this noise and show the results.

8. Repeat the procedure with ECGBaselinedCombined. The signal contains all the above

types of noise combined. Use MATLAB to design a system to eliminate this noise and

show the results.