The study suggests a hybrid Active Noise Cancellation (ANC) system that combines Secondary-path Filtered Active Noise Control (SF-ANC) and a Fuzzy Adaptive Neuro-Fuzzy Inference System (FxANFIS) to improve the noise reduction performance. The approach offers an efficiency of noise cancellation that is 25% greater than what can be achieved using traditional ANC systems, particularly when handling nonlinear and dynamic patterns of noises. Even though the primary focus is audio noise cancellation, the techniques developed here can potentially be applied to image processing domains, such as image signal noise reduction, where adaptive filtering and fuzzy logic may be applied to enhance image quality. Conventional ANC techniques are inadequate when nonlinear and dynamic noise behavior is managed, especially in real-time. The approach in the study responds to this challenge through the utilization of SF-ANC for cancelling broadband noise and supporting it with FxANFIS to deal with the nonlinear dynamics of the noise. A weighted output strategy is utilized with 60% output from SF-ANC and 40% output from FxANFIS, using a hyperbolic tangent function to guarantee stability. The system is deployed on an embedded Raspberry Pi 4 with computation and low-latency capability. Performance comparisons under different acoustic conditions show the suppression of major noise, and the system has realistic applications in real-time areas like industrial noise control and personal audio amplification. The proposed technique provides a balance between accuracy, flexibility, and efficiency compared to traditional ANC techniques.

Active Noise Cancellation Secondary-path Filtered Active Noise Control (SFANC) Filtered x Adaptive Neuro Fuzzy Inference System (FxANFIS) Hybrid Algorithms Embedded Systems Real-time Processing Image Processing