Active noise control without tap length selection: a model order weighting method

Conventional active noise control systems typically rely on fixed tap lengths in control filters. Determining an appropriate tap length is challenging, even within a straightforward time-invariant environment. This challenge is exacerbated when essential system responses, such as primary noise characteristics or primary path responses, remain partially unknown and necessitate the use of an adaptive filter. A trade-off emerges between convergence rate and steady-state performance when selecting a fixed tap length in adaptive filters. In time-varying environments, the optimal tap length may even dynamically shift over time. Thus, prior attempts have been made to introduce variable tap length algorithms to dynamically adapt tap length in real time. However, the performance of variable tap length algorithms can be sensitive to the choice of additional parameters and noise level. This paper exploits a model order weighting approach by combining the outputs of filters of different tap lengths based on their predicted noise control performance. This proposed method demands minimal additional prior information compared to the variable tap length methods. The noise control performance, as demonstrated by specific examples of active noise control, is presented and analyzed.