Adaptive filters use an FIR-equivalent filter for forming the echo path. The echo path is updated with different algorithms of LMS and RLS. In recent implementations, the adaptive filter is split into two parts. In Fig. 6.7, the filters are marked as adaptive as well as the hold filter. The adaptive filter keeps adapting as per the previously explained algorithms. The hold filter gets an update from the adaptive filter in good conditions. Although the adaptive filter is settling, the hold filter keeps taking update. The hold filter will not have any filter coefficient adaptation. The adaptive filter summing junction output is mainly used for the adaptive filter closed-loop adaptation. The hold filter summing junction output is the actual output used with an NLP operation. When the adaptive filter is disturbed, it can reload good coefficients from the hold filter. When there is a near-end signal or a double talk condition, the adaptive filter is not updated. This type of double-filtering scheme minimizes the disturbances during undesired conditions [URL (Cisco-G168)]. The quality of echo removal will be better and stable with this scheme. This type of scheme requires slightly higher memory and processing mainly to validate the updates as well as an additional filtering operation. More aspects of the hold filter in associating with double talk are given in the subsequent part of this chapter.