Real-time digital signal processing: implementations, ... changes in the input signal is limited by its internal clock rate, so that it may be slow to
Document source : notes.ump.edu.my
Table 8.6 (continued )
j j
mpyk #TWOMU, AC0
mov rnd(hi(AC0)), mmap(T1) ; T1 mu*e [n] uen
mpym *AR4, T0, AC0
j j
rptblocal lms_loop-1
; for(j 0; i < NÀ2; i)
macm *AR3+, T1, AC0
; w [i] alpha*w [i] uen*x [i]
mov rnd(hi(AC0)), *AR4
mpym *AR4, T0, AC0
lms_loop
macm *AR3,T1, AC0
; w [NÀ1] a*w [NÀ1] uen*x [NÀ1]
mov rnd(hi(AC0)), *AR4
; Store the last w [i]
mov *AR0+, *AR3
; x [n] in [n]
loop
Figure 8.13 The signal plots of the adaptive linear predictor
The experiment results are shown in Figure 8.13. The input signal x(n) shown in the
top window contains both the broadband random noise and the narrowband sinusoid
signal. The adaptive filter output y(n) consisting of the narrowband sinusoid signal is
shown in the middle window. The adaptive linear predictor output e(n) shown in the
bottom window contains the broadband noise.
EXPERIMENTS USING THE TMS320C55X
393
Summary :
Table 8.6 (continued ) j j mpyk #TWOMU, AC0 mov rnd(hi(AC0)), mmap(T1) ; w [i] alpha*w [i] uen*x [i] mov rnd(hi(AC0)), *AR4 mpym *AR4, T0, AC0 lms_loop macm *AR3,T1, AC0 ;
Tags :
ac0,shown,signal,ar4,adaptie,window,rndhiac0,nà1,predictor,figure,output,uenx,linear