x(n)
y(n)
d(n)
+
-
LMS
algorithm
Unknown
System, H(z)
e(n)
^
H(z)
Random
noise
generator
Figure 9.4 Off-line modeling of an unknown system using adaptive filter
4. Go to step 1 for the next iteration until the adaptive filter
^
Hz converges to the
optimum solution. That is, the power of e(n) is minimized.
After convergence of the algorithm, the adaptation is stopped and coefficients
^
h
l
, l 0, 1, . . . , L À 1 are fixed. It is important to note that an averaging technique
can be used to obtain better results. If the algorithm converges at time n N, the
coefficients are averaged over the next M samples as
^
h
l
1
M
X
NMÀ1
nN
^
h
l
n, l 0, 1, . . . , L À 1:
9:2:10
9.3 DTMF Tone Detection
This section introduces detection methods for DTMF tones used in the communication
networks. The correct detection of a digit requires both a valid tone pair and the correct
timing intervals. DTMF signaling is used both to set up a call and to control features
such as call forwarding and teleconferencing calling. In some applications, it is neces-
sary to detect DTMF signaling in the presence of speech, so it is important that the
speech waveform is not interpreted as valid signaling tones.
9.3.1 Specifications
The implementation of a DTMF receiver involves the detection of the signaling tones,
validation of a correct tone pair, and the timing to determine that a digit is present for
the correct amount of time and with the correct spacing between tones. In addition, it is
necessary to perform additional tests to improve the performance of the decoder in the
presence of speech. A DSP implementation is useful in applications in which the
digitized signal is available and several channels need to be processed such as in a
private branch exchange.
410
PRACTICAL DSP APPLICATIONS IN COMMUNICATIONS