The problem of identification of quasi-periodically varying dynamic systems is considered. This problem can be solved using generalized adaptive notch filtering (GANF) algorithms. It is shown that the accuracy of parameter estimates can be significantly increased if the results obtained from GANF are further processed using a cascade of appropriately designed filters. The resulting generalized adaptive notch smoothing (GANS) algorithm can be employed in many off-line or nearly real-time on-line applications, such as elimination of sinusoidal interference from a prerecorded signal or identification of a rapidly varying telecommunication channel.