Active Noise Control (ANC) Primarily Based on LMS Algorithm with Wavelet Transform Enhancement

In Active Noise Control (ANC) systems, the Least Mean Squares (LMS) algorithm serves as the fundamental adaptive filtering technique. However, its performance becomes suboptimal when handling broadband noise signals in low signal-to-noise ratio scenarios. This limitation primarily stems from the influence of input signal autocorrelation distribution on control efficacy. Wavelet transform demonstrates capability in eliminating signal autocorrelation properties, thus introducing wavelet-based preprocessing to ANC architectures presents a methodological improvement. Code implementations often involve wavelet packet decomposition (using functions like wpdec in MATLAB) to decorrelate input signals before feeding them into the LMS adaptive filter.

Beyond wavelet transform, additional methodologies can enhance ANC system performance. Adaptive filtering techniques like RLS (Recursive Least Squares) or FxLMS (Filtered-x LMS) algorithms can provide improved convergence characteristics. Multichannel ANC systems employing multiple reference sensors and actuators can expand control bandwidth through spatial domain processing. The integration of these approaches typically requires careful system identification and real-time implementation with overlap-add processing for continuous operation.

Therefore, when addressing challenges in ANC systems, incorporating wavelet transform alongside complementary techniques like multichannel adaptive filtering and advanced convergence algorithms can significantly improve control performance. System designers should consider hybrid architectures that combine wavelet-based decorrelation with robust adaptation mechanisms, potentially implemented through frame-based processing with buffer management for real-time applications.