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10.4 Double-Talk Effects and Solutions
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An extremely important issue of designing adaptive echo cancelers is to handle double talk, which occurs when the far-end and near-end talkers are speaking simultaneously. In this case, signal d(n) consists of both echo r (n) and near-end speech u(n) as shown in Figure 10.3. During the double-talk periods, the error signal e(n) described in Equation (10.4) contains the residual echo, the uncorrelated noise v(n), and the near-end speech u(n). To correctly identify the characteristics of P(z), d(n) must originate solely from its input signal x(n). In theory, the far-end signal x(n) is uncorrelated with the near-end speech u(n), and thus will not affect the asymptotic mean value of the adaptive lter coef cients. However, the variation in the lter coef cients about this mean will be increased substantially in the presence of the near-end speech. Thus, the echo cancelation performance is degraded. An unprotected algorithm may exhibit unacceptable behavior during double-talk periods. An effective solution is to detect the occurrence of double talk and then to disable the adaptation of W (z) during the double-talk periods. Note that only the coef cient adaptation as illustrated in Figure 10.12 is disabled. If the echo path does not change during the double-talk periods, the echo can be canceled by the previously converged W (z), whose coef cients are xed during double-talk periods. As shown in Figure 10.12, the speech detection/control block is used to control the adaptation of the adaptive lter W (z) and the nonlinear processor (NLP) that is used for reducing residual echo. The
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double-talk detector (DTD), which detects the presence of near-end speech when the far-end speech is present, is a very critical element in echo cancelers. The conventional DTD based on the echo return loss (ERL), or hybrid loss, can be expressed as = 20 log10 E [|x(n)|] . E [|d(n)|] (10.10)
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Figure 10.12 Adaptive echo canceler with speech detectors and nonlinear processor
In several adaptive echo cancelers such as de ned by ITU standards, the ERL value is assumed to be 6 dB. Based on this assumption, the near-end speech is present if |d(n)| > 1 |x(n)| . 2 (10.11)
However, we cannot just use the instantaneous absolute values |d(n)| and |x(n)| under the noisy condition. A modi ed near-end speech detection algorithm declares the presence of near-end speech if |d(n)| > 1 max {|x(n)| , . . . , |x(n L + 1)|} . 2 (10.12)
Equation (10.12) compares an instantaneous absolute value |d(n)| with the maximum absolute value of x(n) over a time window spanning the echo path. The advantage of using an instantaneous power of d(n) is its fast response to the near-end speech. However, it will increase the probability of false trigger if noise exists in the network. A more robust version of speech detector replaces the instantaneous power |x(n)| and |d(n)| with the short-term power estimates Px (n) and Pd (n). These short-term power estimates are implemented by the rst-order IIR lter as Px (n) = (1 )Px (n 1) + |x(n)| and Pd (n) = (1 )Pd (n 1) + |d(n)| , (10.14) (10.13)
where 0 < << 1. The use of a larger results in robust detector. However, it also causes slower response to the presence of near-end speech. With the modi ed short-term power estimate, the near-end speech is detected if Pd (n) > 1 max {Px (n), Px (n 1), . . . , Px (n L + 1)} . 2 (10.15)
It is important to note that a portion of the initial break-in near-end speech u(n) may not be detected by this detector. Thus, adaptation would proceed in the presence of double talk. Furthermore, the requirement of