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maintaining a buffer to store L-power estimates increases the memory requirement and the complexity of algorithm. The assumption that the ERL is a constant (6 dB) is not always correct. If the ERL is higher than 6 dB, it will take longer time to detect the presence of near-end speech. If the ERL is lower than 6 dB, most far-end speech will be falsely detected as near-end speech. For practical applications, it is better to dynamically estimate the time-varying threshold by observing the signal level of x(n) and d(n) when the near-end speech u(n) is absent.
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10.5 Nonlinear Processor
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The residual echo can be further reduced using an NLP realized as a center clipper. The comfort noise is inserted to minimize the adverse effects of the NLP.
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10.5.1 Center Clipper
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Nonlinearities in the echo path, noise in the circuits, and uncorrelated near-end speech limit the amount of achievable cancelation for a typical adaptive echo canceler. The NLP shown in Figure 10.12 removes the last vestiges of the remaining echoes. The most widely used NLP is a center clipper with the input output characteristic illustrated by Figure 10.13. This nonlinear operation can be expressed as y(n) = 0, x(n), |x(n)| , |x(n)| > (10.16)
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where is the clipping threshold. This center clipper completely eliminates signals below the clipping threshold , but leaves signals greater than the clipping threshold unaffected. A large value of suppresses all the residual echoes but also deteriorates the quality of the near-end speech. Usually the threshold is chosen to be equal or to exceed the peak amplitude of return echo.
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10.5.2 Comfort Noise
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The NLP completely eliminates the residual echo and circuit noise, thus making the connection not real . For example, if the near-end subscriber stops talking, the noise level will suddenly drop to zero since it has been clipped by the NLP. If the difference is signi cant, the far-end subscriber may think the call has
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d(n) + y(n) Noise update e(n) v(n) Comfort noise
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Figure 10.14 Implementation of G.168 with comfort noise insertion
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been disconnected. Therefore, the complete suppression of a signal using NLP has an undesired effect. This problem can be solved by injecting a low-level comfort noise when the residual echo is suppressed. As speci ed by Test 9 of G.168, the comfort noise must match the signal level and frequency contents of background noise. In order to match the spectrum, the comfort noise insertion is implemented in frequency domain by capturing the frequency characteristic of background noise. An alternate approach uses the linear predictive coding (LPC) coef cients to model the spectral information. In this case, the comfort noise is synthesized using a pth-order LPC all-pole lter, where the order p is between 6 and 10. The LPC coef cients are computed during the silence segments. The ITU-T G.168 recommends the level of comfort noise within 2 dB of the near-end noise. An effective way of implementing NLP with comfort noise is shown in Figure 10.14, where the generated comfort noise v(n) or echo canceler output e(n) is selected as the output according to the control logic. The background noise is generated with a matched level and spectrum, heard by the farend subscriber remaining constant during the call connection, and thus signi cantly contributing to the high-grade perceptive speech quality.
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10.6 Acoustic Echo Cancelation
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There has been a growing interest in applying acoustic echo cancelation for hands-free cellular phones in mobile environments and speakerphones in teleconferencing. Acoustic echoes consist of three major components: (1) acoustic energy coupling between the loudspeaker and the microphone; (2) multiplepath sound re ections of far-end speech; and (3) the sound re ections of the near-end speech signal. In this section, we focus on the cancelation of the rst two echo components.