# An Active Indoor Noise Control System Based on CS Algorithm
###### Abstract
Active control has significant advantages at low frequencies when lower sampling rates are appropriate and only plane wave propagation is permitted. Due to the maturation of adaptive-filtering theory and digital signal processors in the 1980s, the development of ANC was rapid. [PERSON] and [PERSON] [9], [PERSON] and [PERSON] [10], and the references therein all contributed to the development of ANC. The filtered-x least-mean-square (FxLMS) method [11] is the most widely used adaptive algorithm in ANC. Since the FxLMS method has a basic structure, it has been widely investigated and expanded. Moreover, the FxLMS method is widely used because of its cheap overhead processing and superior performance than the Volterra filtered-x LMS (VFXLMS) technique [12]. Improved nonlinear ANC algorithms based on the VFXLMS and FSLMS algorithms have recently been developed [13]. The researchers of all these publications assess the performance of their algorithms in the presence of nonlinear main routes and chaotic noise. Many naturally energized algorithms exhibit certain swarm intelligence traits, and in applied research, nature/bit-inspired approaches have been widely exploited for the optimization of many difficult problems. Optimization algorithms must be used to determine the most accurate and optimal solution to a problem when numerical approaches fail. As a result, when the noise level is raised, the FxLMS/FxLMS algorithm fails to work. To mitigate the impact of nonlinearity, a nonlinear-based ANC method is presented, in which the Cuckoo search (CS) algorithm [14] is used to control the parameters of a filter bank structure. For multimodal objective functions, CS was shown to outperform particle swarm optimization (PSO) [15] and genetic algorithms (GA) [16]. This was due in part to the fact that CS has fewer parameters to fine-tune than PSO and GA. As a result, when compared to other metaheuristic algorithms, CS is more flexible and resilient for many optimization tasks. To summarize, this study applies the CS approach to an ANC system. Unlike other typical gradient-based methods, the suggested strategy does not require any secondary path estimate-filtering calculation and so delivers a computational advantage. Many people use headphones with ANC technology at present [17]. ANC may minimize engine noise in high-speed automobiles [18], cars [19], planes [20], and other vehicles. However, there have been few studies on noise reduction in an interior scenario. The causes of indoor ambient noise are discussed in this study, and an ANC system for an indoor application is presented. According to the simulation, it performs better than the suggested system in terms of saturation in tone signals, multi-tone signals, and real noise signals. 1. Finally, we designed the hardware of the system, and carried out experiments to verify the results. The following are some highlights of the study's contributions to innovation:  
1. An ANC system is supposed for use indoors, taking outdoor interference noise into consideration;  
2. A CS-based animal dynamic method is developed for nonlinear adaptive control systems with accuracy, robustness, and stability;  
3. The adaptive control method proposed in this study, based on the CS algorithm, successfully simulates sinusoidal, random, and complex random noise interference in various situations of linear/nonlinear and primary/secondary routes, demonstrating the scheme's worth.