Simulations of Partial Update LMS Algorithms in Application to Active Noise Control

Partial Update LMS (PU LMS) algorithms started to play an important role in adaptive processing of sound. Due to reduction of computational power demands, these algorithms allow to use longer adaptive filters, and therefore achieve better results in adaptive filtering applications, e.g., system identification, acoustic echo cancellation, and active noise control (ANC). There are two main groups of PU algorithms: data-independent and data-dependent algorithms. While application of a data-independent algorithm almost always results in a degradation of performance, application of data-dependent PU algorithms may even result in an increase of the performance, compared with full parameters update. However, the latter group of algorithms requires sorting. A number of updated parameters is the factor that allows to decide how much of performance should be sacrificed to obtain computational power savings. In the extreme case only one filter tap out of possibly hundreds is updated during each sampling period. The goal of this paper is to show extensive simulations proving that careful selection of this one tap results in a useful and well performing algorithm, even in the demanding application of active noise control.