2D-BiSpecNet: Bispectrum Image-Based Convolutional Network for Adaptive Subfilter Selection in Active Noise Control

Abstract

Conventional adaptive active noise control (ANC) techniques, such as filtered-x normalized least mean square (FxNLMS), frequently run into issues when the noise environment changes, leading to longer reaction times. Moreover, fixed-filter approaches may lose the essential phase information necessary for efficient noise cancellation. This paper introduces 2D-BiSpecNet, a novel, effectively delayless feedforward active noise control system that uses a deep learning co-processor to address these difficulties. The technique converts one-dimensional audio signals into 64 × 64 bispectrum matrices, which transform sounds into visual representations. Therefore, it focuses on nonlinear quadratic phase couplings (QPCs), which provide robust and amplitude-independent views of the noise structure. The system then applies a quick multilabel classifier to examine these representations and immediately generates a control filter via 15 parallel subcontrol filters. The paper specifies a 5 × 5 convolutional receptive field that had the maximum efficacy. Simulations with real acoustic data indicate that this configuration yields an average noise reduction of −14.48 dB for aircraft noise, outperforming the usual FxNLMS technique by nearly 6 dB. The technology conducts classification and filtering nearly seven times faster than adaptive approaches, thus reducing convergence delays and delivering a more reliable and low-latency solution for noise-canceling environments.