Lines Matching full:signal

43 adequately cover the duration of that impulse response. The signal transmitted
45 properly adapted, the resulting output is an estimate of the echo signal
46 received from the line. This is subtracted from the received signal. The result
47 is an estimate of the signal which originated at the far end of the line, free
48 from echos of our own transmitted signal.
54 very poorly for things like speech echo cancellation, where the signal level
55 varies widely.  This is quite easy to fix. If the signal level is normalised -
56 similar to applying AGC - LMS can work as well for a signal of varying
57 amplitude as it does for a modem signal. This normalised least mean squares
64 to adapt best to the strongest parts of a signal. If the signal is white noise,
66 high frequency content. Pre-whitening (i.e. filtering the signal to flatten its
67 spectrum) the echo signal improves the adapt rate for speech, and ensures the
68 final residual signal is not heavily biased towards high frequencies. A very
75     - The transmitted signal has poor self-correlation.
76     - There is no signal being generated within the environment being
86 random signal - the impulse response of the line. For a repetitive signal,
95 The adaption process is based on trying to eliminate the received signal. When
96 there is any signal from within the environment being cancelled it may upset
97 the adaption process. Similarly, if the signal we are transmitting is small,
99 adaption is only performed when we are transmitting a significant signal level,
101 we are sending a significant signal. Telling, if the environment is generating
102 a significant signal, and doing it with sufficient speed that the adaption will
107 the received signal, there are a number of strategies which may be used to
108 assess whether that signal contains a strong far end component. However, by the
109 time that assessment is complete the far end signal will have already caused