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Musical-Applications-of-Microprocessors-2ed-Chamberlin-H-1987

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SOURCE-SIGNAL ANALYSIS<br />

575<br />

185491 A INPUT SIGNAL I<br />

AMPLITUDE e-. -= ~ ~ ~ /\ f\ A ~ C\ ~<br />

-13776 ~, v ~'J=_51\Z \[~~ :'J:<br />

o 119<br />

TIME IN SAMPLES<br />

"' 106~ S"",-"ME ,,,m,,<br />

66--.I\~<br />

o 3000<br />

FREQUENCY IN HZ<br />

106~<br />

"' "L ,~ =;;~"m"M<br />

o<br />

FREQUENCY IN<br />

HZ<br />

3000<br />

dB<br />

106~'N=4<br />

66<br />

o 3000<br />

FREQUENCY IN HZ<br />

"' ]--~5~~:;;;:1<br />

o 3000<br />

FR EQUENCY IN HZ<br />

"'W,~<br />

"'1<br />

66~~<br />

o 3000<br />

FREQUENCY IN HZ<br />

",'~:~"I<br />

o 3000<br />

FREQUENCY IN HZ<br />

Fig. 16-15. Effectiveness <strong>of</strong> linear preditive spectral analysis. Source: Digital<br />

Processing <strong>of</strong> Speech Signals, L.R. Rabiner and R.W. Schafer,<br />

Prentice-Hall, 1978.<br />

In the following discussion, the spectrum will in fact be considered as a time<br />

sequence and terms relating to time-sequence processing will be used.<br />

Unfortunately, applying a linear filter to a raw amplitude spectrum is<br />

not really mathematically correct. In the natural sound model, the final

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