A CONCEPTUAL STUDY ON ACTIVE NOISE CANCELLATION: FROM ADAPTIVE ALGORITHMS TO DEEP LEARNING APPROACHES
TL;DR
This conceptual study provides a thorough explanation of ANC technology spanning more than 90 years of development, from Paul Lueg's early 1936 patent to the most recent deep learning methods, and shows that deep learning approaches are the ANC technology of the future.
Abstract
Active Noise Cancellation (ANC) began as a theoretical idea in the 1930s and has now spread to include broad uses in consumer electronics, industrial, and automotive industries. This conceptual study provides a thorough explanation of ANC technology spanning more than 90 years of development, from Paul Lueg's early 1936 patent to the most recent deep learning methods. Adaptive filtering algorithms such as LMS, NLMS, FxLMS, FuLMS, and RLS variants are closely examined, along with new neural network architectures like Convolutional Recurrent Networks (CRN) and Attentive Recurrent Networks (ARN), which achieve sub-4 ms latency with noise cancellation improvements of 5-6 dB over traditional methods. The noise reduction parameters, computational complexity, power consumption, and convergence characteristics are all included in the thorough performance comparisons presented in this study. Using benchmark datasets like NOISEX-92 and DEMAND for algorithm evaluation, we examine feedforward, feedback, and hybrid system designs used in consumer headphones, automobile cabins, aviation, and HVAC systems. Given their greater resistance to broadband noise and nonlinear distortions, our conceptual study shows that deep learning approaches are the ANC technology of the future. However, conventional FxLMS maintains the industry standard because of its computational efficiency (O(N) complexity).