Adaptive Filters and Applications - Digital Signal Processing: Fundamentals and Applications - page 485

float e[1] ¼ {0.0}; /* Enhanced signal */
float mu ¼ 0.000000000004; /*Adaptive filter convergence factor*/
interrupt void c_int11()
{

float lc; /*left channel input */
float rc; /*right channel input */
float lcnew; /*left channel output */
float rcnew; /*right channel output */
int i;
//Left channel and right channel inputs
AIC23_data.combo ¼ input_sample();

lc ¼ (float) (AIC23_data.channel[LEFT]);

(float) (AIC23_data.channel[RIGHT]);
// Insert DSP algorithm below
d[0] ¼ lc; /*Corrupted signal*/
for(i ¼ 19;i>0;i--) /*Update the reference noise buffer input buffer*/
{ x[i] ¼ xn[i-1]; }
x[0] ¼ lc;
// Adaptive filter
y[0] ¼ 0;
for(i ¼ 0;i<20; i þþ )
{ y[0] ¼ y[0] þ w[i]*x[i];}
e[0] ¼ d[0]-y[0]; /* Enhanced output */
for(i ¼ 0;i<20; i þþ )
{ w[i] ¼ w[i] þ 2*mu*e[0]*x[i]; }/* LMS algorithm */
// End of the DSP algorithm
lcnew ¼ e[0]; /* Send to DAC */
rcnew ¼ rc; /* keep the original data */
AIC23_data.channel[LEFT] ¼ (short) lcnew;
AIC23_data.channel[RIGHT] ¼ (short) rcnew;
output_sample(AIC23_data.combo);

rc ¼

}

Many other practical configurations can be implemented similarly.

10.6 SUMMARY
1. Adaptive filters can be applied to signal-changing environments, spectral overlap between noise
and signal, and unknown, or time-varying, noise.
2. Wiener filter theory provides optimal weight solutions based on statistics. It involves collection of
a large block of data, calculation of an autocorrelation matrix and a cross-correlation matrix, and
inversion of a large autocorrelation matrix.
3. The steepest decent algorithm can find the optimal weight solution using an iterative method, so
a large matrix inversion is not needed. But it still requires calculating an autocorrelation matrix
and cross-correlation matrix.
4. The LMS is a sample-based algorithm, which does not need collection of data or computation of
statistics and does not involve matrix inversion.