On Oct 3, 6:54�pm, "steveu" <steveu@n_o_s_p_a_m.coppice.org> wrote:> >On Sep 30, 9:58=A0pm, "steveu" <steveu@n_o_s_p_a_m.coppice.org> wrote: > >> >On Sep 30, 5:35=3DA0am, "steveu" <steveu@n_o_s_p_a_m.coppice.org> > wrote: > > >> >> The > >> >> channel needs to be linear for a canceller to work really well. > > >> >Not true!! If you have a non-linear system and your model of that > >> >system is good, the echo canceller will work very well. Look up > >> >Hammerstein model, for example or Volterra kernels. > > >> Look up "Really great working Hammerstein model" or "Superb results > from > >> Volterra kernel". > > >> >> Distortion in the mics and speakers, or channel non-linearities such > a= > >s > >> G=3D > >> >.711 coding > >> >> on the PSTN, will limit the effectiveness of the echo cancellation. > > >> >a-law and mu-law limit the cancellation primarily becaue of the errors > >> >in coding, not non-linearities. > > >> You must be the first person I've heard call a-law and u-law linear > >> transforms. Linear to log, even pseudo log, is normally considered a > >> non-linear operation. > > >I said nothing about a-law or mu-law being linear! I said the > >cancellation limits in echo cancellers using a-law or-mulaw are > >primarily from the encoding. Even a linear system has cancellation > >limits when encoded. Do a linear-to-log transformation and then a log- > >to-linear inverse transformation WITHOUT coding, and the canceller > >will work very well. Get the model to actually reflect the unknown > >systyem, and the canceller will work very well. > > There is no real coding. There is fairly coarse *quantization* in the > psuedo-log domain. > > > > > > > > >> >> With a good linear channel you might get 60dB of echo reduction, > limit= > >ed > >> =3D > >> >mostly by > >> >> how well you fine tune the canceller. > > >> >The canceller is adaptive. What do you mean by *fine tuning*? > > >> Huh? Do you adapt in fairly large steps, to pull in quickly? Do you > adapt > >> in fine steps, to get really close to perfect? Do you switch adaption > rat= > >es > >> to pull in quickly, and finely tune later on? How finely do you > eventuall= > >y > >> approximate the perfect canceller? > > >> Steve > > >The change in the update gain can (and usually does) affect the speed > >of convergence and residule noise (called misalignment) from an > >adaptive filter. When the update gain is varied, as you have > >suggested, it is usually to achieve convergence speed at the beginning > >of the adaptive process, and then low residule noise after > >convergence. I know of some adaptive systems that monitor the residule > >and other factor to vary the gain if the impulse response changes. > >However, I don't know what YOU meant by *fine tuning* the cancaller. > >To me fine tuning means tuning very carefully to get right on the > >target. If the target is 10, and I estimate 10 +/- 5 or I estimate 10 > >+/- 1 or I estimate 10 +/- 0.001, I am still estimating the target to > >be 10. I am not *tuning* between 9 and 10 and 11. To me there is a > >difference between estimating something with a tolerance, and tuning > >something into the estimate. Thus the question, what do YOU mean by > >fine tuning? > > Because you can play games, like switched adaption rates, to balance > performance criteria such as convergence rate and final quality of > adaption, I think many people might describe this as tuning. I know when > designing cancellers I certainly think of this as tuning the design for > best balanced performance. > > Steve- Hide quoted text - > > - Show quoted text -- Hide quoted text - > > - Show quoted text -Steve, I just needed to know what YOU meant by *tuning*. Remember, the connotation of words very often differs with different people. Your connotation of tuning and mine are not the same. I also think there is a difference between tuing the design, and tuning the filter. Now that I known what your connotation is, we can go from there. Yes, the adaptive filter may be designed to vary the update gain for specific performance parameters. The most basic example of this is the move from the LMS algorithm to the NLMS algorithm. The NLMS varies the update gain automatically, to compensate for input power level changes. Another design parameter may be the type of input signal: wideband, narrowband, noise, sinusoidal. Another, may be the susceptibility of the algorithm to noise, especially impulse noise, e.g. T. I Haweel and P. M. Clarkson, "Analysis and generalization of a median adaptive filter", Proc. IEEE ICASSP, pp. 1269 - 1272, 1990 M. Givens, P. M. Clarkson, "The application of the median LMS algorithm to ADPCM systems", Proc. IEEE ICASSP, pp. 3665 - 3668, 1991 There have been lots of attempts at gain varying to control some aspect of performance, however, I haven't seen the *holy grail* yet. Maurice Givens