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CS6422 Datasheet(PDF) 34 Page - Cirrus Logic |
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CS6422 Datasheet(HTML) 34 Page - Cirrus Logic |
34 / 48 page CS6422 34 characteristics than white noise because of its qua- si-periodic nature. Research at Crystal has shown that quasi-periodic signals cause the formation of spurious non-zero coefficients within the adaptive filter at tap inter- vals determined by the periodicity of the signal. This results in small changes in period being very destructive to the adaptive filter’s performance. One mechanism the CS6422 uses to prevent this filter corruption with speech is to pre-emphasize the signal sent to the adaptive filter so that much of the low frequency content is removed. The CS6422 works very well with a speech training signal because of the pre-emphasis filter. White noise training signals, however, result in sub-opti- mal performance, so when testing with white noise, it is recommended that the pre-emphasis filters be disabled. 4.1.1.2.2 Graded Beta The update gain of an adaptive filter, sometimes called the “beta”, is the rate at which the filter co- efficients can change. If beta is too low, the adap- tive filter will be slow to adapt. Conversely, if it is too high, the filter will be unstable and will create unwanted noise in the system. In most echo canceller implementations, the beta is a fixed value for all the filter coefficients. In some situations, though, through knowledge of the char- acteristics of echo path response, the beta can be varied for groups of coefficients. This preserves stability by allowing the beta to be higher for some coefficients and compensating by reducing beta be- low nominal for others. For example, acoustic echo tends to decay expo- nentially, so the first taps need to be larger than the later taps. Having a beta profile that matches the expected response path enhances the echo cancel- ler’s ability to correctly and accurately model the acoustic path. Furthermore, this has an added ben- efit of suppressing the spurious taps mentioned in Section 4.1.1.2.1, “Pre-Emphasis”. The Microcontroller Interface allows four settings for graded beta: none, 0.19 dB/ms, 0.38 dB/ms, and 0.75 dB/ms. Use 0.75 dB/ms for acoustically dead rooms or cars, and 0.19 dB/ms or no grading of beta for large, or acoustically live rooms. 4.1.1.3 Update Control As mentioned in Section 4.1.1.1, “Theory of Oper- ation”, the update control algorithms are the heart of any useful echo canceller implementation. Aside from telling the adaptive filter when to adapt, they are responsible for correcting performance when the path changes more quickly than the filter can respond. For example, if the adaptive filter is actu- ally adding signal power instead of cancelling it, the update control algorithms will reset the adap- tive filter to cleared coefficients, forcing it to re- start. 4.1.1.4 Speech Detection The CS6422 detects speech by using power estima- tors to track deviations from a background noise power level. The power estimators filter and aver- age the raw incoming samples from the ADC. A background noise level is established by a regis- ter that increases 3 dB at intervals determined by NseRmp (Register 2, bits 11 and 10). When the power estimator level rises, the background noise level will slowly increase to try to match it. When the power estimator level is below the background noise level, the background noise level adjusts quickly to match the power estimator level. This method allows significant flexibility in tracking the background noise level. Speech is detected when the power estimator level rises above the background noise level by a given threshold. The half-duplex receive speech detector threshold is set by RHDet (Register 2, bits 15 and 14), the half-duplex transmit speech detector threshold is set by THDet (Register 1, bits 15 and CS6422 34 DS295F1 |
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