Active noise cancellation gets a boost: A novel diffusion-based approach in spline adaptive filters.

Abstract

This study addresses the challenge of unwanted noise in signal processing, particularly for applications requiring high-fidelity audio like noise-canceling headphones. Current adaptive filters offer some noise reduction but struggle with specific noise profiles. We propose the enhanced adaptive filter and a distributed learning utilizing a novel diffusion-based framework that leverages spline adaptation. This innovative approach integrates a natural logarithm and a special function called the hyperbolic cosine for improved noise cancellation. Our method achieves superior noise reduction and improved signal quality compared to existing techniques. This research demonstrates the effectiveness of the enhanced adaptive filter, making it ideal for applications demanding pristine audio and well-suited for distributed noise cancellation scenarios.